Management system, management device, and management method

Abstract

A management device (100) acquires the wave height and the wind speed at the installation site of a wind turbine generator (20). The management device (100) generates individual prediction information on the basis of prediction information from a weather server (80), the wave height, and the wind speed. The management device (100) then determines a maintenance schedule for the wind turbine generator (20) on the basis of the individual prediction information.

Description

Management system, management device, and management method

[0001] The present disclosure relates to a management system, a management device, and a management method.

[0002] For example, Japanese Patent Application Laid-Open No. 2013-185507 (Patent Document 1) discloses a condition monitoring system for a wind power generation device. This condition monitoring system includes a vibration sensor that detects the vibration value of a target position of the wind power generation device. The condition monitoring system diagnoses the presence or absence of an abnormality based on the vibration value detected by the vibration sensor. Then, the condition monitoring system displays the diagnosis result on the display unit of the monitoring terminal.

[0003] Japanese Patent Application Laid-Open No. 2013-185507

[0004] By the way, there is a wind power generation device installed offshore. When the above-mentioned abnormality of such a wind power generation device is detected, for example, the maintenance planner refers to weather forecasts and the like provided by a weather server or the like to determine the maintenance schedule and the content of the maintenance. Then, the maintenance performer moves to the wind power generation device by a ship or the like on the maintenance schedule and performs maintenance on this wind power generation device.

[0005] However, when the accuracy of the weather forecast is poor, there may be a case where the waves are high or the wind is strong at the installation location of the wind power generation device in the maintenance schedule. In this case, there may arise a problem that the maintenance performer cannot appropriately perform the maintenance of the wind power generation device.

[0006] The present disclosure has been made to solve the above problems, and an object thereof is to determine a maintenance schedule that makes it easy for a maintenance performer to perform the maintenance of a wind power generation device installed offshore.

[0007] The management system disclosed herein comprises a wind turbine, a measuring device, and a management device. The wind turbine is installed offshore. The measuring device measures at least one of a first physical quantity relating to waves at the installation site of the wind turbine and a second physical quantity relating to wind at the installation site. The management device determines the maintenance content of the wind turbine using predetermined data for determining the maintenance content of the wind turbine, and also determines the future maintenance schedule for the wind turbine. The management device obtains prediction information from an external server, which shows predictions for physical quantities corresponding to the measured physical quantities measured by the measuring device from among the first and second physical quantities. The management device executes a process to generate individual prediction information from the measured physical quantities and the prediction information. The management device determines the maintenance schedule based on the individual prediction information.

[0008] The management device disclosed herein comprises an interface and a control device. The interface acquires at least one physical quantity from a first physical quantity relating to waves at the installation site of a wind power generation device installed offshore and a second physical quantity relating to wind at the installation site. The control device determines the maintenance content of the wind power generation device using predetermined data for determining the maintenance content of the wind power generation device, and also determines the future maintenance schedule of the wind power generation device. The control device acquires prediction information from an external server, which shows predictions of physical quantities corresponding to the measured physical quantities acquired by the interface from the first physical quantity and the second physical quantity. The control device executes a process to generate individual prediction information from the measured physical quantities and the prediction information. The control device determines the maintenance schedule based on the individual prediction information.

[0009] The management method disclosed herein comprises acquiring at least one physical quantity from a first physical quantity relating to waves at the installation site of a wind power generation device installed offshore and a second physical quantity relating to wind at the installation site. The management method comprises determining maintenance content using predetermined data for determining maintenance content for the wind power generation device. The management method comprises acquiring prediction information from an external server, which shows predictions of physical quantities corresponding to the acquired measured physical quantities from the first and second physical quantities. The management method comprises executing a process to generate individual prediction information from the measured physical quantities and prediction information. The management method comprises determining future maintenance schedules for the wind power generation device based on the individual prediction information.

[0010] According to this disclosure, in the diagnosis of a wind power generation system, it is possible to determine a maintenance schedule that makes it easier for the maintenance operator to perform maintenance on the wind power generation system.

[0011] This is a diagram illustrating an example of a management system configuration. This is a functional block diagram of the management device. This is a diagram to explain data assimilation. This is a schematic diagram of a weather database. This is a schematic diagram of a maintenance database. This is a diagram showing an example of an execution table. This is a flowchart of the generation process. This is a flowchart of the maintenance process. This is a flowchart of the anomaly diagnosis process. This is a diagram to explain data assimilation.

[0012] Embodiments of the present invention will be described below with reference to the drawings. In the following drawings, identical or corresponding parts will be given the same reference numeral, and their descriptions will not be repeated.

[0013] [Example of Management System Configuration] Figure 1 shows an example of the configuration of the management system 10 of this embodiment. The management system 10 of this disclosure is a condition monitoring system for monitoring the status of a wind power generation device.

[0014] The management system 10 of this disclosure comprises an anomaly monitor 30, a wave monitor 35, M (where M is an integer of 1 or more) wind power generation units 45, a user terminal 50, a maintenance terminal 70, a management device 100, and a network NW. The management device 100 can communicate with the anomaly monitor 30, the wave monitor 35, the wind sensor 53, the user terminal 50, and the maintenance terminal 70 via the network NW. The user terminal 50 corresponds to the "predetermined terminal" of this disclosure. The management system 10 can also communicate with a weather server 80. In Figure 1, an example is shown in which the weather server 80 is installed outside the management device 100, but as a modification, a configuration in which the weather server 80 is included in the management device 100 may be adopted.

[0015] In this embodiment, the management device 100 functions as a cloud server. However, as a modification, the management device 100 may function as a local server.

[0016] The wind power generation unit 45 comprises a wind power generation device 20, at least one wave sensor 52, at least one wind sensor 53, and N vibration sensors Sn (n = 1, ..., N, where N is an integer of 1 or more).

[0017] The wind turbine 20 is a device that generates electricity by receiving wind power. The wind turbine 20 has a base 21. The wind turbine 20 is installed offshore by the base 21. The wind turbine 20 is also called an "offshore wind turbine". The wind turbine 20 has a main bearing section, a generator, and a speed increaser, etc. Each of the M wind turbines 20 is assigned identification information (ID). The identification information is information used to identify the wind turbine 20. The base 21 is, for example, a floating or fixed-bottom base.

[0018] Each vibration sensor Sn detects the vibration value of a diagnostic target location (for example, the main bearing, speed increaser, and generator) of the wind power generation device 20. The vibration value is represented by, for example, the displacement, velocity, and acceleration of the predetermined location. The vibration values ​​detected by each vibration sensor Sn of the M wind power generation units 45, along with the sensor ID of the vibration sensor, are associated and output to the abnormality monitor 30 as time-series data. The time-series data may include the rotational speed of the rotating body of the bearing of the wind power generation device 20, and the amount of power generated by the wind power generation device 20. The time-series data corresponds to the "predetermined data" in this disclosure.

[0019] The time-series data collected by the anomaly monitor 30 and the wind power generation device ID of the wind power generation device 20 from which the time-series data was obtained are associated and output to the management device 100.

[0020] A wave sensor 52 is installed on the base 21 of the wind power generation device 20. The wave sensor 52 measures at least one of wave height and wave period on or underwater. At least one of wave height and wave period corresponds to the "first physical quantity" of this disclosure. In this embodiment, the wave sensor 52 detects wave height. The wave sensor 52 is attached, for example, to the base 21 or a buoy.

[0021] The wave sensor 52 is equipped with, for example, an acceleration sensor, and the wave sensor 52 detects wave height based on the value detected by this acceleration sensor. The wave sensor 52 may also be a radar-type wave meter or the like.

[0022] The wave heights detected by the wave sensors 52 of each of the M wind power generation units 45 are collected by the wave monitor 35. The wave monitor 35 transmits the collected wave heights to the management device 100.

[0023] The wind sensor 53 is installed on the wind power generation device 20. The wind sensor 53 detects the wind direction and wind speed to the wind power generation device 20. The wind sensor 53 is, for example, an anemometer. The wind speed detected by each wind sensor 53 of the M wind power generation units 45 is transmitted to the control device 100. The wind speed corresponds to the “second physical quantity” of this disclosure.

[0024] The control device 100 controls the wind power generator 20 using the wind speed obtained from the wind sensor 53. For example, if the wind speed exceeds a threshold, the control device 100 stops the operation of the wind power generator 20 in order to prevent malfunction of the wind power generator 20.

[0025] As will be described later, the management device 100 generates individual forecast information, which will be described later, using the wind speed acquired from the wind sensor 53 and the forecast information acquired from the weather server 80 (hereinafter also referred to as "acquired forecast information"). In other words, the wind speed acquired by the wind sensor 53 is used for both controlling the wind power generation device 20 and generating individual forecast information.

[0026] In this way, the control device 100 can acquire wind speed and wave height in the installation area, including the installation location of the wind power generation device 20. The wind sensor 53 and wave sensor 52 are also referred to as the "measuring device 60". Furthermore, the control device 100 acquires wind speed and wave height using SCADA (Supervisory Control And Data Acquisition).

[0027] The control device 100 determines whether or not there is an abnormality in the wind turbine 20 based on time-series data from the abnormality monitor 30. If the control device 100 detects an abnormality in the wind turbine 20, it determines the maintenance required to resolve the abnormality. Thus, the time-series data is used to determine the maintenance required for the wind turbine. The maintenance may include, for example, replacing the bearings of the wind turbine 20 or replacing the lubricant used in the bearings.

[0028] Furthermore, the control device 100 determines the maintenance schedule. The maintenance schedule is the date on which future maintenance will be performed on the wind turbine that has been found to have an abnormality. In addition, the control device 100 creates a maintenance estimate for the wind turbine 20.

[0029] The management device 100 includes a control device 102, a memory 104, and an interface 106. The control device 102 performs various processes and calculations. Each component is interconnected by a data bus. The memory 104 includes ROM (Read Only Memory) and RAM (Random Access Memory), among others.

[0030] The control device 102 consists of a CPU (Central Processing Unit), an FPGA (Field-Programmable Gate Array), and a GPU (Graphics Processing Unit), among others. The control device 102 may consist of at least one of the CPU, FPGA, and GPU. Furthermore, the control device 102 may consist of processing circuitry. The control device 102 is also referred to as "at least one processor" or "processing circuitry."

[0031] The memory 104 includes a volatile storage area (for example, a working area) for temporarily storing program code, work memory, etc., when the control device 102 executes an arbitrary program. For example, the memory 104 includes RAM (Random Access Memory) and ROM (Read Only Memory).

[0032] ROM stores the program executed by the control device 102. RAM temporarily stores data generated by the execution of the program in the control device 102. RAM can function as a temporary data memory used as a working area.

[0033] Interface 106 is configured to communicate with external devices of the management device 100 (such as an anomaly monitor 30, a user terminal 50, and a maintenance terminal 70).

[0034] The user terminal 50 is a terminal device owned by user A. "User A" is typically the owner of the wind power generation equipment 20, for example, a power generation business operator. The user terminal 50 is typically a portable terminal that user A can carry with them. The user terminal 50 may also be a dedicated computer terminal.

[0035] The management device 100 transmits the above maintenance estimate to the user terminal 50. The user terminal 50 displays the maintenance estimate on its display unit, thereby allowing user A to recognize the maintenance estimate and other related information.

[0036] When the control device 100 detects an abnormality in the wind power generation equipment 20, it determines the maintenance schedule and maintenance details based on the type of abnormality. The control device 100 then sends a request signal to the maintenance terminal 70 to request maintenance on the determined maintenance schedule and with the determined maintenance details.

[0037] When the maintenance terminal 70 receives a request signal, the administrator of the maintenance terminal 70 arranges for a maintenance executor E. The maintenance executor E then travels to the wind turbine 20 by boat or other means and performs maintenance on the wind turbine 20.

[0038] The weather server 80 provides forecast information for waves and wind over a wide area, including the area where the wind power generation device 20 is installed. This area corresponds to the “first area” of this disclosure.

[0039] The forecast information is, for example, based on the Global Spectral Model (GSM). The weather server 80 is, for example, the Japan Meteorological Agency or the U.S. Environmental Forecasting Service. The weather server 80 is also referred to as the "external server".

[0040] Generally, from the standpoint of work efficiency, it is preferable for the maintenance operator E to perform maintenance on the offshore wind turbine 20 when the wave height is low or the wind is weak at the installation site. Therefore, a skilled maintenance planner creates a maintenance schedule based on their own experience, after checking statistical data on past sea conditions at the installation site of the wind turbine 20 or the latest weather forecast. However, creating this maintenance schedule requires a considerable amount of manpower and the experience of the maintenance planner.

[0041] In particular, large-scale construction work such as replacing the main bearings or speed increasers of the wind turbine 20 requires the arrangement of special work vessels such as SEP (Self Elevating Platform) ships or crane ships.

[0042] Therefore, if maintenance work is delayed due to high wave heights or strong winds, additional costs such as the rental fee for work vessels will be incurred. For this reason, it is necessary to plan maintenance schedules that are less likely to be delayed. In addition, offshore wind turbines 20 have less time available for maintenance compared to wind turbines installed on land.

[0043] Therefore, the management device 100 improves the accuracy of the forecast information from the weather server 80. Specifically, the management device 100 periodically (for example, every hour) acquires forecast information from the weather server 80. The management device 100 then executes a generation process to generate individual forecast information in order to improve the accuracy of the forecast information for the installation location of the wind power generation device 20. The individual forecast information is forecast information that shows the forecast of the corresponding physical quantity in the area including the installation location of the wind power generation device 20. This area corresponds to the "second area" in this disclosure. In this embodiment, the first area and the second area are assumed to be the same size.

[0044] [Functional Block Diagram] FIG. 2 is a functional block diagram of the management device 100. The management device 100 includes a receiving unit 112, a processing unit 114, a transmitting unit 116, and a storage unit 118. The receiving unit 112 and the transmitting unit 116 correspond to the interface 106 in FIG. 1. The processing unit 114 corresponds to the control device 102 in FIG. 1. The storage unit 118 corresponds to the memory 104 in FIG. 1, and at least a part of the storage area of the memory 104 is applied.

[0045] The storage unit 118 stores a weather DB (Data Base) 141 and a maintenance DB 142.

[0046] The receiving unit 112 acquires time-series data from the abnormality monitor 30, a correction request (to be described later) from the user terminal 50, prediction information from the weather server 80, the wind speed from the wind sensor 53, and the wave height from the wave monitor 35. Also, the storage unit 118 stores past wind speeds and past wave heights, and the past wind speeds and past wave heights may be used in data assimilation (to be described later).

[0047] The processing unit 114 periodically acquires prediction information from the weather server 80. Then, the processing unit 114 generates individual prediction information with improved accuracy of the prediction information for the installation location of the wind power generation device 20. The processing unit 114 generates individual prediction information using the wind speed from the wind sensor 53 and the wave height from the wave sensor 52. The processing unit 114 stores the individual prediction information in the weather DB 141.

[0048] The processing unit 114 can acquire, for example, future wind speeds and wave heights at the installation location of the wind power generation device 20 by referring to the weather DB 141 in which the individual prediction information is stored. Since the weather DB 141 stores individual prediction information with improved accuracy, the processing unit 114 can acquire wind speeds and wave heights with good accuracy.

[0049] The processing unit 114 detects abnormalities in the wind power generation equipment 20 based on time-series data. When the processing unit 114 detects an abnormality in the wind power generation equipment 20, it determines the maintenance content based on the type of abnormality. Furthermore, the processing unit 114 determines the maintenance schedule based on the weather DB 141 and the maintenance content. In addition, the processing unit 114 calculates a maintenance estimate based on the maintenance content and other factors by referring to the estimation DB (not shown). The estimation DB is, for example, a DB in which estimated amounts are defined for each maintenance item.

[0050] The processing unit 114 generates a request signal to request maintenance executor E to perform maintenance according to the maintenance details and schedule. The request signal includes the maintenance details and schedule. Then, the transmission unit 116 transmits the request signal to the maintenance terminal 70. Maintenance executor E, upon viewing the request signal displayed on the maintenance terminal 70, can recognize the maintenance details and schedule.

[0051] Furthermore, the processing unit 114 generates maintenance information, including a maintenance estimate. The maintenance information may include at least one of the following: maintenance details and maintenance schedule. The transmission unit 116 transmits the maintenance information to the user terminal 50. User A, upon viewing the maintenance information displayed on the user terminal 50, can recognize the maintenance estimate and other details.

[0052] [Generation Process] Next, the generation process of individual forecast information by the processing unit 114 will be described. In this embodiment, the processing unit 114 generates individual forecast information using data assimilation. Data assimilation refers to improving the accuracy of a weather model (for example, the GSM described above) by inputting actual measured values ​​into the weather model. The measured values ​​are the wind speed obtained from the wind sensor 53 and the wind speed obtained from the wave monitor 35. The measured values ​​correspond to the "measured physical quantities" in this disclosure. In addition, data assimilation methods include the four-dimensional variational method, the three-dimensional variational method, and the optimal interpolation method.

[0053] Figure 3 is a diagram illustrating data assimilation. Figure 3(A) shows the measurement location by the measuring device 60, Figure 3(B) shows the acquired prediction information, and Figure 3(C) shows the individual prediction information. In Figures 3(A) to (C), multiple squares are shown, and each square represents the smallest area indicated by the GSM. In Figure 3, the first location P1 and the second location P2 are shown.

[0054] Furthermore, in Figure 3, the cells of the sensor S indicate the measurement location, and the sensor S represents a wind sensor 53 or a wave sensor 52, etc. Also, the hatching of each cell indicates, for example, wave height or wind speed. The wave height or wind speed at the first location P1 corresponds to the "physical quantity predicted at the first location" in this disclosure, and the wave height or wind speed at the second location P2 corresponds to the "physical quantity predicted at the second location" in this disclosure.

[0055] The processing unit 114 applies data assimilation to the measured values ​​(Figure 3(A)) and acquired prediction information (Figure 3(B)) to generate individual prediction information (Figure 3(C)). This allows the processing unit 114 to interpolate the predicted wind speed and wave height in the region between the first point P1 and the second point P2 based on the measured values ​​from the wind sensor 53 and the wave sensor 52, respectively. In the examples in Figures 3(B) and 3(C), the wave height or wind speed in the region between the first point P1 and the second point P2 has been modified. In this way, the processing unit 114 can generate individual prediction information that locally indicates the prediction information for the installation area, including the installation location of the wind power generation device 20. Furthermore, as explained in Figure 7, the management device 100 performs the individual prediction information generation process periodically (for example, every hour).

[0056] [DB] Next, the weather DB 141 and maintenance DB 142 of the management device 100 will be described. Figure 4 is a schematic diagram of the weather DB 141. In the example in Figure 4, the future wind speed B and wave height C for each date and time A of each unit period are shown. For example, the unit period is, for example, one hour. In other words, the weather DB 141 in Figure 4 specifies the wind speed B and wave height C for each hour. For example, for date and time A1, the wind speed B1 and wave height C1 are specified. Also, as described above, each time individual forecast information is generated, the weather DB 141 is updated based on this individual forecast information.

[0057] Figure 5 is a schematic diagram of the maintenance DB 142. In the example in Figure 5, the schedules of maintenance personnel E and equipment F for each unit period are defined. Equipment F includes tools used for maintenance, replacement parts to be replaced, and a boat for transporting to the wind turbine 20. In this embodiment, the unit period is one day. That is, Figure 5 defines the schedules of maintenance personnel E and equipment F for each day of schedule D. For example, on schedule D1, maintenance personnel E1 and E2 are available for maintenance, and equipment F1 is available. On the other hand, on schedule D2, the schedules of all maintenance personnel and equipment are full, making maintenance impossible. The management device 100 also periodically obtains the latest maintenance DB from, for example, the maintenance terminal 70 and stores it in the storage unit 118 as the maintenance DB 142.

[0058] Furthermore, the management device 100 maintains an execution table. Figure 6 shows an example of an execution table. In the example in Figure 6, individual prediction information is associated with feasible maintenance. High waves / strong winds and low waves / weak winds are shown as individual prediction information.

[0059] When the forecast based on individual forecast information predicts low waves and light winds, heavy maintenance and light maintenance are specified as feasible maintenance. Heavy maintenance is maintenance that places a heavy workload on the maintenance performer E, such as replacing the large bearings of the wind turbine 20. Light maintenance is maintenance that places a lighter workload on the maintenance performer E than heavy maintenance. Light maintenance is, for example, replacing the lubricant in the bearings of the wind turbine 20.

[0060] On the other hand, when individual forecast information predicts high waves and strong winds, minor maintenance is specified as feasible maintenance.

[0061] Light maintenance corresponds to "First Maintenance" in this disclosure, and heavy maintenance corresponds to "Second Maintenance" in this disclosure. In addition, "low waves and light winds" corresponds to "specified conditions" in this disclosure.

[0062] [Flowchart] Figure 7 is a flowchart of the generation process. As described above, the management device 100 executes the generation process shown in Figure 7 as an interrupt process at regular intervals. In step S2, the management device 100 obtains forecast information from the weather server 80. Next, in step S4, the management device 100 obtains wind speed from the wind sensor 53 and wave height from the wave monitor 35 (wave sensor 52). Then, in step S6, the management device 100 generates individual forecast information by data assimilation based on the wind speed, wave height, and acquired forecast information.

[0063] Figure 8 is a flowchart of the operations performed by the control device 100. The control device 100 performs the operations shown in this flowchart at regular intervals. In step S52, the control device 100 performs an abnormality diagnosis process. The abnormality diagnosis process is a process that detects whether or not there is an abnormality in the wind power generation equipment 20 that is being diagnosed.

[0064] Figure 9 is a flowchart of the anomaly diagnosis process in step S52. In step S102, the control device 100 acquires time-series data. Next, in step S104, the control device 100 calculates features from the time-series data. Here, the features include at least one of the following in the time-series data: RMS value, peak value, crest factor, kurtosis, skewness, OA (Overall Value) value, and mean value. Furthermore, unprocessed time-series data (raw waveform data) may be used to calculate the features, or time-series data to which a bandpass filter has been applied may be used.

[0065] Next, in step S106, the management device 100 determines whether the feature quantity is greater than a predetermined threshold. The threshold value used corresponds to the type of feature quantity being compared. If the feature quantity is less than or equal to the threshold (NO in step S106), it is determined that there is no abnormality in the wind power generation device 20, and the abnormality diagnosis process is terminated.

[0066] Furthermore, if the feature quantity is above a threshold (YES in step S106), in step S108, the management device 100 performs frequency analysis on the time series data. For example, the management device 100 performs frequency analysis by converting time-domain time series data into frequency-domain data (for example, a Fast Fourier Transform).

[0067] Next, in step S110, the control device 100 determines whether or not an abnormality has been detected in the wind power generation device 20 by frequency analysis. An abnormality is detected, for example, when envelope analysis is performed and the frequency of the abnormal vibration that caused the threshold to be exceeded matches the frequency of the damaged vibration given from the internal specifications of the damaged part.

[0068] If no abnormality is detected (NO in step S110), the abnormality diagnosis process is terminated. On the other hand, if an abnormality is detected (YES in step S110), in step S112, the management device 100 identifies the timing of component failure (remaining lifespan of the component) based on the abnormality. The remaining lifespan of the component is the number of rotations or hours during which it can continue to operate. The identification of the timing of component failure is performed, for example, using AI (Artificial Intelligence). Furthermore, in step S114, the management device 100 identifies the maintenance content corresponding to the abnormality.

[0069] Let's return to the explanation in Figure 8. When the process in step S52 is completed, in step S54, the management device 100 determines whether or not an abnormality has been detected by the abnormality diagnosis process in step S52.

[0070] Next, in step S56, the management device 100 determines a maintenance schedule. Here, the management device 100 determines a maintenance schedule that satisfies all of conditions A to C. Condition A is that the maintenance schedule is earlier than the failure time identified in step S112. Condition B is that the maintenance schedule is such that, after referring to the maintenance DB (see Figure 5), the maintenance executor E determined in step S114 is available to perform the work and the equipment used for the maintenance is available.

[0071] Condition C is the condition that the maintenance schedule corresponds to the date (see Figure 6) of the individual forecast information, which is determined in step S114. For example, if the maintenance type determined in step S114 is heavy maintenance, the maintenance schedule will be the date with low waves and light winds that corresponds to the heavy maintenance. Also, if the maintenance type determined in step S114 is light maintenance, the maintenance schedule may be either a date with low waves and light winds, or a date with high waves and strong winds.

[0072] Next, in step S58, the management device 100 determines the maintenance estimate and transmits the maintenance information to the user terminal 50. Next, in step S60, the management device 100 determines whether or not the user has a request for correction. A request for correction is, for example, a request to correct the maintenance information. For example, if the user feels that the maintenance estimate included in the maintenance information is too high, the user enters a request for correction into the user terminal 50.

[0073] In step S60, if the management device 100 determines that there is a correction request (YES in step S60), the process returns to step S56. As a result, the management device 100 determines the maintenance schedule and maintenance estimate again. On the other hand, if the management device 100 determines that there is no correction request (NO in step S60), in step S62, the management device 100 sends a maintenance request signal to the maintenance terminal 70.

[0074] [Summary] (1) In the past, if the accuracy of forecast information from weather servers was poor, there was a possibility that the waves would be high or the wind would be strong at the wind turbine installation site on the day of maintenance for the offshore wind turbine. In this case, the maintenance personnel may not be able to properly perform the maintenance on the wind turbine.

[0075] In contrast, the management device 100 of this embodiment generates highly accurate individual prediction information and determines the maintenance schedule based on this individual prediction information. Therefore, the management device 100 can determine a maintenance schedule and maintenance content that is easy for the maintenance operator to perform on the wind power generation equipment 20, thereby suppressing the occurrence of the above-mentioned problems.

[0076] (2) Furthermore, as shown in Figure 3, the generation process includes a process of interpolating the wind speed and wave height predicted in the region between the first point P1 and the second point P2 by applying data assimilation based on measured values. Therefore, the management device 100 can appropriately generate individual prediction information.

[0077] (3) In step S114, the management device 100 determines the maintenance content, and then determines the maintenance schedule in step S56. If the management device 100 decides to perform heavy maintenance in step S114, it refers to the individual forecast information and determines a date with low waves and light winds (see Figure 6) as the maintenance schedule (see the explanation of condition C above). With this configuration, even if heavy maintenance of the wind power generation equipment 20 is required, a maintenance schedule with low waves and light winds is determined, so that the maintenance operator E can perform the heavy maintenance appropriately.

[0078] (4) In step S112, the control device 100 estimates the timing of component failure of the wind power generation device 20. The control device 100 then determines a maintenance schedule prior to this failure time (see condition A above). Therefore, the control device 100 ensures that the maintenance operator E can perform maintenance before any component of the wind power generation device 20 fails, thereby increasing the operating rate of the wind power generation device 20.

[0079] (5) In step S58, the management device 100 determines the estimated amount and transmits the estimated amount to the user terminal 50. Thus, the management device 100 can make user A aware of the estimated amount.

[0080] (6) In step S62, the management device 100 transmits maintenance information to the maintenance terminal 70. Therefore, the management device 100 can eliminate the inconvenience of, for example, user A having to submit a maintenance request.

[0081] [Modifications] (1) In the above embodiment, the management device 100 was configured to generate individual forecast information from the weather server 80 using data assimilation. However, other methods may be used to generate individual forecast information. For example, the management device 100 may use AI to generate individual forecast information.

[0082] (2) In the above embodiment, a configuration was described in which the measuring device 60 is equipped with both a wind sensor 53 and a wave sensor 52. However, a configuration in which the measuring device 60 is equipped with either the wind sensor 53 or the wave sensor 52 may be adopted. If such a configuration is adopted, the management device 100 obtains forecast information from the weather server 80 that shows the forecast of the physical quantity corresponding to the physical quantity measured by the measuring device 60 among wave height and wind speed. For example, if the measuring device 60 is equipped with a wave sensor 52 but not a wind sensor 53, the management device 100 obtains forecast information from the weather server 80 that shows the forecast of the physical quantity (wave height) corresponding to the physical quantity measured by the measuring device 60 (wave height).

[0083] (3) In the example in Figure 3, a configuration was described in which the area indicated by the acquired prediction information (first area) and the area indicated by the individual prediction information (second area) are the same size. However, if the individual prediction information includes the area where the wind power generation device 20 is installed, a configuration in which the area of ​​the individual prediction information is smaller than the area of ​​the acquired prediction information may be adopted. Figure 10 is a diagram showing the individual prediction information when this configuration is adopted.

[0084] As shown in Figure 10, the area of ​​individual prediction information is smaller than the area of ​​acquired prediction information. For example, the processing unit 114 extracts a small area from the acquired prediction information that includes the installation location of the wind power generation device 20 or the installation location of the measuring device 60, and generates individual prediction information based on the extracted area and the measured values.

[0085] With this configuration, the amount of data in the individual prediction information can be reduced compared to the area indicated by the acquired prediction information, which is the same area as the individual prediction information. Therefore, the burden on the processing unit 114 to determine the maintenance schedule can be reduced.

[0086] [Note] (Note 1) A management system comprising: a wind power generation device installed offshore; a measuring device for measuring at least one of a first physical quantity relating to waves at the installation site of the wind power generation device and a second physical quantity relating to wind at the installation site; and a management device for determining maintenance content using predetermined data for determining maintenance content for the wind power generation device, and for determining future maintenance schedules for the wind power generation device, wherein the management device obtains prediction information from an external server that shows predictions of physical quantities corresponding to the measured physical quantities measured by the measuring device among the first and second physical quantities, executes a process to generate individual prediction information from the measured physical quantities and the prediction information, and determines the maintenance schedule based on the individual prediction information.

[0087] (Note 2) The management system according to Note 1, wherein the prediction information includes a physical quantity corresponding to the measured physical quantity predicted at the first location and a physical quantity corresponding to the measured physical quantity predicted at the second location, and the process for generating the individual prediction information includes a process for interpolating the physical quantity corresponding to the measured physical quantity predicted in the region between the first location and the second location by applying data assimilation based on the physical quantity measured by the measuring device.

[0088] (Note 3) The management system described in Note 1 or Note 2, wherein the maintenance of the wind power generation equipment includes a first maintenance and a second maintenance which has a greater maintenance burden than the first maintenance, and the management device determines the maintenance schedule after determining the maintenance content, and if the maintenance content is to perform the second maintenance, it determines the maintenance schedule as a date on which the physical quantities predicted in the individual prediction information satisfy predetermined conditions relating to the feasibility of performing the second maintenance.

[0089] (Note 4) The management device is the management system described in any one of Notes 1 to 3, which estimates the failure time of the components of the wind power generation device based on the predetermined data and determines a time earlier than the failure time as the maintenance schedule.

[0090] (Note 5) The management system further comprises a maintenance database for identifying maintenance personnel who can perform maintenance and equipment available for use in maintenance for each unit period, and the management device determines the maintenance schedule based on the maintenance database, as described in any one of Notes 1 to 4.

[0091] (Note 6) The management device is a management system according to any one of Notes 1 to 5, which determines the estimated cost of maintenance for the wind power generation equipment and transmits the estimated cost to a designated terminal.

[0092] (Note 7) The management device is the management system described in any one of Notes 1 to 6, wherein the management device transmits a request signal to the maintenance terminal to request the maintenance executor to perform the maintenance according to the maintenance details and the maintenance schedule.

[0093] (Appendix 8) The management system according to any one of Appendix 1 to 7, wherein the first physical quantity includes at least one of wave height and wave period.

[0094] (Note 9) The management system described in any one of Notes 1 to 8, wherein the second physical quantity is wind speed.

[0095] (Note 10) The prediction information is information showing a prediction of a physical quantity corresponding to the measured physical quantity in a first region including the installation location of the wind power generation device, and the individual prediction information is information showing a prediction of a physical quantity corresponding to the measured physical quantity in a second region including the installation location of the wind power generation device, and the second region is narrower than the first region, and is the management system described in any one of Notes 1 to 9.

[0096] (Note 11) A management device comprising: an interface for acquiring at least one physical quantity from a first physical quantity relating to waves at the installation site of a wind power generation device installed offshore and a second physical quantity relating to wind at the installation site; and a control device for determining maintenance content using predetermined data for determining maintenance content of the wind power generation device and determining future maintenance schedules for the wind power generation device, wherein the control device acquires prediction information from an external server that shows predictions of physical quantities corresponding to the measured physical quantities acquired by the interface from the first physical quantity and the second physical quantity, executes a process to generate individual prediction information from the measured physical quantities and the prediction information, and determines the maintenance schedule based on the individual prediction information.

[0097] (Note 12) A management method comprising: acquiring at least one physical quantity from a first physical quantity relating to waves at the installation site of a wind power generation device installed offshore and a second physical quantity relating to wind at the installation site; determining maintenance content using predetermined data for determining maintenance content for the wind power generation device; acquiring prediction information from an external server that shows predictions of physical quantities corresponding to the acquired measured physical quantities from the first physical quantity and the second physical quantity; executing a process to generate individual prediction information from the measured physical quantities and the prediction information; and determining the future maintenance schedule for the wind power generation device based on the individual prediction information.

[0098] The embodiments disclosed herein should be considered in all respects to be illustrative and not restrictive. The scope of the present invention is indicated by the claims rather than by the description of the embodiments above, and all modifications within the meaning and scope of the claims are intended to be included.

[0099] 10 Management system, 20 Wind power generation device, 21 Foundation unit, 30 Anomaly monitor, 35 Wave monitor, 45 Wind power generation unit, 50 User terminal, 52 Wave sensor, 53 Wind sensor, 60 Measuring device, 70 Maintenance terminal, 80 Weather server, 100 Management device, 102 Control device, 104 Memory, 106 Interface, 112 Receiving unit, 114 Processing unit, 116 Transmitting unit, 118 Storage unit.

Claims

1. A management system comprising: a wind turbine installed offshore; a measuring device for measuring at least one of a first physical quantity relating to waves at the installation site of the wind turbine and a second physical quantity relating to wind at the installation site; and a management device for determining maintenance content using predetermined data for determining maintenance content for the wind turbine and for determining future maintenance schedules for the wind turbine, wherein the management device obtains prediction information from an external server that shows predictions of physical quantities corresponding to the measured physical quantities measured by the measuring device among the first and second physical quantities; executes a process to generate individual prediction information from the measured physical quantities and the prediction information; and determines the maintenance schedule based on the individual prediction information.

2. The management system according to claim 1, wherein the prediction information includes a physical quantity corresponding to the measured physical quantity predicted at a first location and a physical quantity corresponding to the measured physical quantity predicted at a second location, and the process for generating the individual prediction information includes a process for interpolating the physical quantity corresponding to the measured physical quantity predicted in the region between the first location and the second location by applying data assimilation based on the physical quantity measured by the measuring device.

3. The management system according to claim 1 or 2, wherein the maintenance of the wind power generation equipment includes a first maintenance and a second maintenance which has a greater maintenance burden than the first maintenance, and the management device determines the maintenance schedule after determining the maintenance content, and if the maintenance content is to perform the second maintenance, it determines the maintenance schedule as a date on which the physical quantities predicted in the individual prediction information satisfy predetermined conditions relating to the feasibility of performing the second maintenance.

4. The management system according to claim 1 or 2, wherein the management device estimates the failure time of the components of the wind power generation system based on the predetermined data, and determines a time earlier than the failure time as the maintenance schedule.

5. The management system according to claim 1 or 2, further comprising a maintenance database for identifying maintenance personnel capable of performing maintenance and equipment available for maintenance for each unit period, wherein the management device determines the maintenance schedule based on the maintenance database.

6. The management system according to claim 1 or 2, wherein the management device determines the estimated cost of maintenance for the wind power generation equipment and transmits the estimated cost to a predetermined terminal.

7. The management system according to claim 1 or 2, wherein the management device transmits a request signal to the maintenance terminal to request the maintenance executor to perform the maintenance according to the maintenance details and maintenance schedule.

8. The management system according to claim 1 or 2, wherein the first physical quantity includes at least one of wave height and wave period.

9. The management system according to claim 1 or claim 2, wherein the second physical quantity is wind speed.

10. The management system according to claim 1 or 2, wherein the prediction information is information showing a prediction of a physical quantity corresponding to the measured physical quantity in a first region including the installation location of the wind power generation device, and the individual prediction information is information showing a prediction of a physical quantity corresponding to the measured physical quantity in a second region including the installation location of the wind power generation device, and the second region is narrower than the first region.

11. A management device comprising: an interface for acquiring at least one physical quantity from a first physical quantity relating to waves at the installation site of a wind power generation device installed offshore and a second physical quantity relating to wind at the installation site; and a control device for determining maintenance content using predetermined data for determining maintenance content of the wind power generation device, and for determining future maintenance schedules for the wind power generation device, wherein the control device acquires prediction information from an external server that shows predictions of physical quantities corresponding to the measured physical quantities acquired by the interface from the first physical quantity and the second physical quantity, executes a process to generate individual prediction information from the measured physical quantities and the prediction information, and determines the maintenance schedule based on the individual prediction information.

12. A management method comprising: acquiring at least one physical quantity from a first physical quantity relating to waves at the installation site of a wind power generation device installed offshore and a second physical quantity relating to wind at the installation site; determining maintenance content using predetermined data for determining maintenance content for the wind power generation device; acquiring prediction information from an external server that shows predictions of physical quantities corresponding to the acquired measured physical quantities from the first and second physical quantities; executing a process to generate individual prediction information from the measured physical quantities and the prediction information; and determining future maintenance schedules for the wind power generation device based on the individual prediction information.

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