Management system, management device, and management method

Abstract

Determine maintenance schedules and procedures that are easy for maintenance personnel to perform on offshore wind power generation equipment. [Solution] The management device 100 acquires wave height and wind speed at the installation site of the wind power generation equipment 20. The management device 100 generates individual forecast information based on the forecast information from the weather server 80 and the wave height and wind speed. Then, the management device 100 determines the maintenance schedule for the wind power generation equipment 20 based on the individual forecast information.

Description

Technical Field

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

Background Art

[0002] For example, Japanese Unexamined Patent Application Publication 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.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By the way, there is a wind power generation device installed offshore. When an abnormality of such a wind power generation device is detected, for example, maintenance planners determine the maintenance schedule and content by referring to weather forecasts provided by a weather server or the like. Then, maintenance workers move to the wind power generation device by ship or the like on the maintenance schedule and perform maintenance on this wind power generation device.

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

[0006] This disclosure is made to solve the above-mentioned problems, and its purpose is to determine a maintenance schedule that makes it easier for maintenance personnel to perform maintenance on offshore wind power generation equipment. [Means for solving the problem]

[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 among the first and second physical quantities. 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 that 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. [Effects of the Invention]

[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. [Brief explanation of the drawing]

[0011] [Figure 1] This diagram shows an example of a management system configuration. [Figure 2] This is a functional block diagram of the control device. [Figure 3] This is a diagram to explain data assimilation. [Figure 4] This is a schematic diagram illustrating the weather database. [Figure 5] This is a schematic diagram illustrating the maintenance database. [Figure 6] This figure shows an example of an execution table. [Figure 7] This is a flowchart of the generation process. [Figure 8] This is a flowchart of the maintenance process. [Figure 9] This is a flowchart for the anomaly diagnosis process. [Figure 10] This is a diagram to explain data assimilation. [Modes for carrying out the invention]

[0012] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the following drawings, the same or corresponding parts are denoted by the same reference numerals, and their descriptions will not be repeated.

[0013] [Configuration Example of Management System] FIG. 1 is a diagram showing a configuration example of a management system 10 according to the present embodiment. The management system 10 of the present disclosure is a condition monitoring system for monitoring the state of a wind power generation device.

[0014] The management system 10 of the present disclosure includes an abnormality monitor 30, a wave monitor 35, M (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 each other through the abnormality monitor 30, the wave monitor 35, a wind sensor 53, the user terminal 50, the maintenance terminal 70, and the network NW. The user terminal 50 corresponds to the "predetermined terminal" of the present disclosure. Also, the management system 10 can communicate with a weather server 80. In FIG. 1, an example in which the weather server 80 is installed outside the management device 100 is shown. However, as a modification, a configuration in which the weather server 80 is included in the management device 100 may be adopted.

[0015] In the present 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 includes 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, N is an integer of 1 or more).

[0017] The wind power generation device 20 is a device that generates electricity by receiving wind power. The wind power generation device 20 has a foundation part 21. The wind power generation device 20 is installed on the ocean by the foundation part 21. The wind power generation device 20 is also referred to as an "offshore wind power generation device". The wind power generation device 20 has a main bearing part, a generator, a speed increaser, and the like. Each of the M wind power generation devices 20 is given identification information (ID: Identification). The identification information is information for identifying the wind power generation device 20. The foundation part 21 is, for example, a float or a gravity-based foundation.

[0018] Each of the vibration sensors Sn detects the vibration value of a diagnosis target location (for example, the main bearing part, the speed increaser, and the generator) of the wind power generation device 20. The vibration value is represented by, for example, any one of displacement, speed, and acceleration at the predetermined location. The vibration value detected by each vibration sensor Sn of the M wind power generation units 45 and 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, the power generation amount of the wind power generation device 20, and the like. The time series data corresponds to the "predetermined data" of the present disclosure.

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

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

[0021] For example, an acceleration sensor is installed in the wave sensor 52, and based on the detection value of this acceleration sensor, the wave sensor 52 detects the wave height. The wave sensor 52 may be a radar type wave gauge or the like.

[0022] The wave heights detected by the wave sensors 52 of each of the M wind turbine 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 turbine 20. The wind sensor 53 detects the wind direction and wind speed to the wind turbine 20. The wind sensor 53 is, for example, an anemometer. The wind speed detected by each wind sensor 53 of the M wind turbine 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 the individual forecast information described below 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 the 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 equipment 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 unit 102, a memory 104, and an interface 106. The control unit 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 unit 102 consists of a CPU (Central Processing Unit), an FPGA (Field-Programmable Gate Array), and a GPU (Graphics Processing Unit), among others. The control unit 102 may also consist of at least one of the CPU, FPGA, and GPU. Furthermore, the control unit 102 may consist of processing circuitry. The control unit 102 is also referred to as "at least one processor" or "processing circuitry."

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

[0032] ROM stores the program executed by the control unit 102. RAM temporarily stores data generated by the execution of the program in the control unit 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 the abnormality monitor 30, user terminal 50, and maintenance terminal 70).

[0034] 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. User terminal 50 is typically a portable terminal that user A can carry with them. Note that 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 turbine 20 is installed. This area corresponds to the “first area” of this disclosure.

[0039] The forecast information is, for example, based on the GSM (Global Spectral Model). Weather server 80 is, for example, the Japan Meteorological Agency or the U.S. Environmental Forecasting Service. Weather server 80 is also referred to as an "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 wind turbines 20 requires the arrangement of specialized 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 equipment 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 equipment 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] Figure 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 Figure 1. The processing unit 114 corresponds to the control device 102 in Figure 1. The storage unit 118 corresponds to the memory 104 in Figure 1, and at least a portion of the storage area of ​​the memory 104 is used.

[0045] The memory unit 118 stores the weather database 141 and the maintenance database 142.

[0046] The receiving unit 112 acquires time-series data from the anomaly monitor 30, correction requests from the user terminal 50 (described later), forecast information from the weather server 80, wind speed from the wind sensor 53, and wave height from the wave monitor 35. The storage unit 118 also stores past wind speed and past wave height, and these past wind speed and past wave height may be used in the data assimilation described later.

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

[0048] The processing unit 114 can, for example, obtain future wind speed and wave height for the installation site of the wind power generation device 20 by referring to the weather DB 141, which stores individual forecast information. Since the weather DB 141 stores improved individual forecast information, the processing unit 114 can obtain highly accurate wind speed and wave height.

[0049] The processing unit 114 detects abnormalities in the wind power generation equipment 20 based on time-series data. If 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 database in which estimated costs 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 process of generating 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 4D variational method, the 3D variational method, and the optimal interpolation method.

[0053] Figure 3 is a diagram illustrating data assimilation. Figure 3(A) shows the measurement locations 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, with each square representing 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. 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 from the measured values ​​(Figure 3(A)) and the 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 date and time A for 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, whenever 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 processes executed by the control device 100. The control device 100 executes the processes 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 are any abnormalities 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, the calculation of features may use unprocessed time-series data (raw waveform data) or time-series data to which a bandpass filter has been applied.

[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 control device 100 performs frequency analysis on the time series data. For example, the control 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, based on the reference of the maintenance DB (see Figure 5), the maintenance executor E determined in step S114 is available to 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, where the type of maintenance determined in step S114 is heavy maintenance. For example, if the type of maintenance determined in step S114 is heavy maintenance, the maintenance schedule will be the date with low waves and light winds corresponding to that heavy maintenance. Also, if the type of maintenance 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) Conventionally, if the accuracy of forecast information from weather servers is poor, there may be high waves or strong winds 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 addition, the control device 100 determines the maintenance content in step S114, and then determines the maintenance schedule in step S56. If the control 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 turbine 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 turbine 20 fails, thereby increasing the operating rate of the wind turbine 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] [Differentiation] (1) In the above embodiment, a configuration was described in which the management device 100 generates 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) Wind power generation equipment installed offshore, A measuring device for measuring at least one of the following physical quantities: 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. The system includes a management device that determines the maintenance content of the wind power generation equipment using predetermined data for determining the maintenance content of the wind power generation equipment, and also determines the future maintenance schedule of the wind power generation equipment. The aforementioned control device is Prediction information is obtained from an external server, which shows the prediction of the physical quantity corresponding to the physical quantity measured by the measuring device among the first physical quantity and the second physical quantity. The process of generating individual prediction information from the measured physical quantity and the prediction information is executed. A management system that determines the maintenance schedule based on the aforementioned individual prediction information.

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

[0088] (Note 3) Maintenance of the aforementioned wind power generation equipment is First maintenance and, This includes a second maintenance that has a greater maintenance burden than the first maintenance, The aforementioned control device is After determining the maintenance details, the maintenance schedule will be determined. The management system described in Appendix 1 or Appendix 2, wherein if the maintenance content is to perform the second maintenance, the management system determines the maintenance date as a date on which the physical quantities predicted in the individual prediction information satisfy predetermined conditions for the second maintenance to be performable.

[0089] (Note 4) The aforementioned control device is Based on the predetermined data, the timing of failure of the components of the wind power generation device is estimated. A management system described in any one of the appendices 1 to 3, which determines the maintenance schedule to be a period prior to the aforementioned failure time.

[0090] (Note 5) The aforementioned management system is Furthermore, a maintenance database is provided to identify maintenance personnel who can perform maintenance and equipment available for maintenance for each unit period. The management device is a management system according to any one of the appendices 1 to 4, which determines the maintenance schedule based on the maintenance database.

[0091] (Note 6) The management device is a management system according to any one of the appendices 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 described in any one of the appendices 1 to 6 transmits a request signal to the maintenance terminal to request the maintenance executor to perform the maintenance according to the maintenance details and schedule.

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

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

[0095] (Note 10) The aforementioned prediction information is information that shows the 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. The aforementioned individual prediction information is information that shows the prediction of the physical quantity corresponding to the measured physical quantity in the second region, which includes the installation location of the wind power generation device. The second area is a management system described in any one of the appendices 1 to 9, which is narrower than the first area.

[0096] (Note 11) 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, The system includes a control device that determines the maintenance content using predetermined data for determining the maintenance content of the wind power generation equipment, and also determines the future maintenance schedule for the wind power generation equipment. The control device is Prediction information is obtained from an external server, which shows the prediction of the physical quantity corresponding to the measured physical quantity acquired by the interface among the first physical quantity and the second physical quantity. The process of generating individual prediction information from the measured physical quantity and the prediction information is executed. A management device that determines the maintenance schedule based on the aforementioned individual prediction information.

[0097] (Note 12) To obtain at least one of the following physical quantities: 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 maintenance details are determined using predetermined data for determining the maintenance details of the wind power generation equipment, Obtaining prediction information from an external server that shows the prediction of the physical quantity corresponding to the measured physical quantity among the first and second physical quantities, The process involves generating individual prediction information from the measured physical quantity and the prediction information, A management method comprising determining the future maintenance schedule for the wind power generation equipment based on the individual forecast 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 equivalent to the claims are intended to be included. [Explanation of symbols]

[0099] 10 Management system, 20 Wind turbine, 21 Foundation, 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. Wind power generation equipment 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, The system includes a management device that determines the maintenance content of the wind power generation equipment using predetermined data for determining the maintenance content of the wind power generation equipment, and also determines the future maintenance schedule of the wind power generation equipment. The aforementioned control device is Prediction information is obtained from an external server, which shows the prediction of the physical quantity corresponding to the physical quantity measured by the measuring device among the first physical quantity and the second physical quantity. The process of generating individual prediction information from the measured physical quantity and the prediction information is executed. A management system that determines the maintenance schedule based on the aforementioned individual prediction information.

2. 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. The management system according to claim 1, wherein 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 point and the second point by applying data assimilation based on the physical quantity measured by the measuring device.

3. Maintenance of the aforementioned wind power generation equipment is First maintenance and, This includes a second maintenance that has a greater maintenance burden than the first maintenance, The aforementioned control device is After determining the maintenance details, the maintenance schedule will be determined. The management system according to claim 1 or 2, wherein if the maintenance content is to perform the second maintenance, the system determines a maintenance schedule 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 aforementioned control device is Based on the predetermined data, the timing of failure of the components of the wind power generation device is estimated. The management system according to claim 1 or claim 2, wherein the maintenance schedule is determined to be a time earlier than the time of failure.

5. The aforementioned management system is Furthermore, a maintenance database is provided to identify maintenance personnel who can perform maintenance and equipment available for maintenance for each unit period. The management device determines the maintenance schedule based on the maintenance database, as described in claim 1 or claim 2.

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 schedule.

8. The management system according to claim 1 or claim 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 aforementioned prediction information is information that shows the 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. The aforementioned individual prediction information is information that shows the prediction of a physical quantity corresponding to the measured physical quantity in the second region, which includes the installation location of the wind power generation device. The management system according to claim 1 or claim 2, wherein the second region is narrower than the first region.

11. 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, The system includes a control device that determines the maintenance content using predetermined data for determining the maintenance content of the wind power generation equipment, and also determines the future maintenance schedule for the wind power generation equipment. The control device is Prediction information is obtained from an external server, which shows the prediction of the physical quantity corresponding to the measured physical quantity acquired by the interface among the first physical quantity and the second physical quantity. The process of generating individual prediction information from the measured physical quantity and the prediction information is executed. A management device that determines the maintenance schedule based on the aforementioned individual prediction information.

12. To obtain at least one of the following physical quantities: 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 maintenance details are determined using predetermined data for determining the maintenance details of the wind power generation equipment, Obtaining prediction information from an external server that shows the prediction of the physical quantity corresponding to the measured physical quantity among the first and second physical quantities, The process involves generating individual prediction information from the measured physical quantity and the prediction information, A management method comprising determining the future maintenance schedule for the wind power generation equipment based on the individual forecast information.

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