System, method and program for predicting timing at which wind turbine blade should be repaired

Abstract

To provide a system and the like for making it possible to repair a wind turbine blade at the proper timing.SOLUTION: The present invention provides a system for predicting the timing at which a wind turbine blade should be repaired. The system comprises: reception means for receiving data representing the surface shape of a wind turbine blade; and prediction means for predicting the timing on the basis of the data. The prediction means is configured to: select a prediction model on the basis of the state of a damaged portion of a surface of the blade indicated in the data and predict the timing on the basis of the data using the selected prediction model.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a system, method, and program for predicting when repairs should be performed on wind turbine blades. [Background technology]

[0002] The nacelle of a large wind turbine is approximately 100 meters tall. The peripheral speed of the blade tips of a large wind turbine is approximately 100 to 120 m / s, and the leading edge (the edge of the blade that cuts through the wind) wears down by approximately 20 μm per year. The blades of large wind turbines are also susceptible to damage from lightning. For this reason, the blades are equipped with lightning receptors to channel lightning current to the ground as a lightning protection measure. However, if the receptors malfunction and conduct poorly, the blades will be unable to channel the lightning current from a lightning strike to the ground, resulting in severe damage. Therefore, the blades of large wind turbines require regular maintenance, and any damaged parts must be repaired.

[0003] A device for performing maintenance on wind turbine blades is known (Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2018 / 155704 Summary of the Invention [Problem to be solved by the invention]

[0005] The object is to provide a system etc. that enables wind turbine blade repairs to be carried out at an appropriate time. [Means for solving the problem]

[0006] The present invention provides a system that uses data representing the surface shape of wind turbine blades to predict the timing for repairing wind turbine blades.

[0007] The present invention provides, for example, the following items. (Item 1) A system for predicting when to repair a wind turbine blade, comprising: receiving means for receiving data representing the surface shape of the blades of the wind turbine; a prediction means for predicting the timing based on the data; A system comprising: (Item 2) The prediction means selecting a predictive model based on the characteristics of the damaged portion of the surface of the blade as indicated in the data; predicting the timing based on the data using the selected prediction model; and The system according to the preceding item is configured to perform the following. (Item 3) The system of any one of the preceding items, wherein the aspect of the damaged portion includes an erosion pattern of the damaged portion. (Item 4) selecting the predictive model comprises: selecting a first predictive model when the damaged portion has an erosion pattern extending from a primary damage portion in a direction perpendicular to the surface; selecting a second predictive model when the damaged portion has an erosion pattern extending from a primary damage portion in a direction parallel to the surface; The system according to any one of the preceding items, comprising at least: (Item 5) 10. The system of claim 9, wherein the data representing the surface shape of the wind turbine blade includes a depth, width, and location of a damaged portion of the surface of the blade. (Item 6) the receiving means further receives environmental data representing an environment around the wind turbine; The system according to any one of the preceding items, wherein the prediction means predicts the timing based on the data and the environmental data. (Item 7) The system of any one of the preceding items, wherein the environmental data includes at least one of wind speed data, rainfall data, raindrop data, UV data, and data representing a location. (Item 8) the receiving means further receives material data representing a material of at least a portion of the blade; The system according to any one of the preceding items, wherein the prediction means predicts the timing based on the data, the environmental data, and the material data. (Item 9) The system described in any one of the preceding items, wherein the material data includes at least one of paint data representing a material of paint on the surface of the blade and putty data representing a material of putty on the surface of the blade. (Item 10) 1. A method for predicting when repairs should be performed on a blade of a wind turbine, comprising: receiving data representing a surface shape of a blade of a wind turbine; predicting the timing based on the data; and A method comprising: (Item 10A) 11. The method according to claim 10, comprising the features according to any one of the preceding claims. (Item 11) A program for predicting timing for repairing a blade of a wind turbine, the program being executed on a computer system including a processor, the program comprising: receiving data representing a surface shape of a blade of a wind turbine; predicting the timing based on the data; and A program causing the processor to perform processing including the steps of: (Item 11A) Item 12. A program according to item 11, comprising the features according to any one of the preceding items. (Item 11B) A non-transitory computer-readable storage medium storing the program according to item 11 or item 11A. [Effects of the Invention]

[0008] According to the present invention, it is possible to provide a system for predicting the timing for repairing wind turbine blades, thereby enabling wind turbine blade repairs to be carried out at the appropriate time and reducing the effort and cost required for repairs. [Brief explanation of the drawings]

[0009] [Figure 1A] A diagram explaining the procedure for maintaining wind turbine blades [Figure 1B] A diagram explaining the procedure for maintaining wind turbine blades [Figure 1C] A diagram explaining the procedure for maintaining wind turbine blades [Figure 1D] A diagram explaining the procedure for maintaining wind turbine blades [Figure 2] FIG. 1 shows an example of a flow according to the system 100 of the present invention. [Figure 3] FIG. 1 shows an example of the configuration of a system 100 according to the present invention. [Figure 4A] A diagram showing an example of data representing the surface shape of a blade plotted on a two-dimensional plane. [Figure 4B] FIG. 1 shows an example of the structure of a neural network model that constructs a prediction model that can be used by the prediction means 120. [Figure 5] Schematic diagram showing two examples of erosion patterns [Figure 6] FIG. 1 is a diagram showing an example of the configuration of an information processing device 200 that implements the system 100. [Figure 7A] A flowchart showing an example of a process 700 in the system 100 of the present invention. [Figure 7B] 7 is a flowchart showing an example of the process of step S702 in process 700. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0011] 1. Wind turbine blade maintenance The inventors of the present invention have been conducting extensive research into a device for performing maintenance on wind turbine blades. The device is designed to autonomously perform maintenance on wind turbine blades.

[0012] The procedure for performing maintenance on wind turbine blades using this device will be described with reference to Figures 1A to 1D. Figures 1A to 1D (a) show the front of wind turbine 10, and Figures 1A to 1D (b) show the right side of wind turbine 10. Figures 1A to 1D (b) show only blade 11, the object of maintenance, and the other two blades 12 and 13 are omitted. The wind turbine shown in Figures 1A to 1D (a) rotates clockwise on the drawing, and the straight edge of each blade is the leading edge.

[0013] In this specification, the term "wind turbine" refers to a device that receives wind to obtain power. An example of a wind turbine is a wind power generator.

[0014] As used herein, "maintenance" refers to the inspection or upkeep of an object. Maintenance includes repair. Examples of maintenance include taking images of the surface of an object, inspecting the electrical continuity of the object's lightning receptor, cleaning the surface of the object, grinding and / or polishing the surface of the object, applying paint to the surface of the object, and applying a material such as putty, adhesive, or sealant to the surface of the object. Repair may include cleaning a damaged area to remove foreign matter, applying a material such as putty, adhesive, or sealant to the damaged area after cleaning, polishing the applied material to smoothly connect it to the undamaged area, and applying paint to the polished surface or attaching a protective material to the polished surface.

[0015] FIG. 1A shows the device 50 in a preparatory stage before it is attached to the blade 11 of the wind turbine 10.

[0016] Maintenance of the blade 11 is performed with the blade 11 positioned so that it extends vertically downward. This is the same state as the blade is in when a worker performs maintenance by climbing a rope stretched over the blade and moving along the blade, and is therefore easily accepted at existing work sites. When the blade 11 is positioned so that it extends vertically downward, the leading edge is inclined at approximately 5 degrees relative to the vertical, as shown in FIG. 1A(b).

[0017] In a preparation stage before the device 50 is attached to the blades 11 of the wind turbine 10, the ropes 20 are installed on the wind turbine 10. Here, the manner in which the ropes 20 are installed on the wind turbine 10 is not important. The ropes 20 can be installed at any position on the wind turbine 10 by any method.

[0018] In the following, an example will be described in which two ropes 20 are fixed to the nacelle 14 of the wind turbine 10. Each of the two ropes 20 extends from the nacelle 14 of the wind turbine 10, passing near the hub 15, to the ground. The hub 15 is a member that rotatably couples the blades 11, 12, and 13 to the nacelle 14.

[0019] 1B and 1C show the stage in which the device 50 is placed on the blade 11 of the wind turbine 10. FIG.

[0020] Two ropes 20 extend from the nacelle 14 of the wind turbine 10 to the device 50. The two ropes 20 are connected to the device 50.

[0021] The device 50 is equipped with a moving means for moving along the two ropes 20. The moving means is, for example, a winch. The device 50 is equipped with, for example, two winches, with one rope connected to one of the two winches and the other rope connected to the other of the two winches. One end of the rope 20 is fixed to the ground by, for example, a weight or the like. The device 50 can ascend along the rope 20 by winding up the rope 20 using the winch.

[0022] When the device 50 rises along the rope 20 and reaches approximately the same height as the tip of the blade 11, the spatial position of the device 50 is controlled so that the device 50 rests on the tip of the blade 11. The spatial position of the device 50 can be controlled by any method.

[0023] Preferably, the spatial position of the device 50 is controlled by pulling one or both of the two ropes 20 from the ground, because this allows the spatial position of the device 50 to be controlled safely from the ground without requiring any additional equipment.

[0024] As shown in FIG. 1C, once the device 50 reaches the blade 11, the device 50 can be attached to the blade 11 by any mechanism.

[0025] The device 50 also includes a moving means for moving over the blade 11. The moving means is, for example, a wheel. The device 50 can move over the blade, for example, by using a winch to wind or unwind the rope 20 while the wheel is in contact with the blade 11.

[0026] FIG. 1D shows the device 50 moving along the rope 20 over the leading edge of the blade 11.

[0027] As the device 50 moves along the leading edge, the device 50 can maintain a state of being attached to the blade 11. This allows the device 50 to move on the leading edge of the blade 11 without lifting up. Furthermore, as described above, since the leading edge of the blade 11 is inclined at about 5 degrees (in some cases, about 1 degree to about 10 degrees) from the vertical, gravity acting on the device 50 acts to press the device 50 against the leading edge of the blade 11, preventing the device 50 from lifting up.

[0028] The device 50 performs maintenance on the blade 11 while moving over the leading edge of the blade 11 .

[0029] By controlling the winding and unwinding of the winch, the device 50 can move over the leading edge of the blade 11 in the direction in which the rope 20 extends, for example, from the tip of the blade toward the base of the blade and from the base of the blade toward the tip of the blade. This allows the device 50 to perform maintenance while moving back and forth over the leading edge of the blade 11. Furthermore, even if the device 50 should become detached from the blade 11, the rope 20 connected to the winch of the device 50 serves as a lifeline, preventing the device 50 from falling.

[0030] Furthermore, the device 50 can measure the surface shape of the blade 11 using a scanner while moving over the leading edge of the blade 11. The scanner can be any means capable of measuring the surface shape (or profile) of the blade, such as a laser scanner, an ultrasonic scanner, etc. A contact sensor may be used instead of the scanner. The scanner can measure the surface shape with a predetermined resolution.

[0031] The surface shape of the blade measured using a scanner can accurately represent the unevenness of the blade surface. For example, if there is a damaged portion on the blade surface, data representing the surface shape of the blade 11 obtained by measurement using a scanner can include the depth, position, and width of the damaged portion. This makes it possible to accurately understand the state of the damaged portion. The data representing the surface shape of the blade 11 obtained by measurement can be used by the system 100 of the present invention.

[0032] For example, if there is a damaged portion on the surface of blade 11, conventionally, a worker would approach the blade and visually inspect it, or would view a two-dimensional image captured by a camera, and the worker would then determine, based on their experience, whether the damaged portion needed repair. The two-dimensional image could be captured by a camera mounted on a maintenance device attached to the blade, such as device 50, or by a camera mounted on a device that approaches and observes the blade (e.g., an aerial vehicle such as a drone). In this case, the accuracy of the judgment could not be guaranteed because it relied on the worker's experience. Furthermore, the exact nature of the damaged portion may not be apparent from the two-dimensional image, which also affects the accuracy of the judgment. Furthermore, obtaining two-dimensional images requires periodically attaching a device to the blade or approaching the blade, which incurs regular costs.

[0033] As a result of extensive research, the inventors of the present invention have developed a system that can predict when blade repairs should be performed by utilizing the surface shape (or profile) of the blade. This system can objectively predict when repairs should be performed, without relying on the experience of an expert. Furthermore, this system can predict not only whether repairs to damaged areas are currently necessary, but also when they will be necessary in the future, eliminating the need to periodically inspect the blade surface and reducing recurring costs.

[0034] FIG. 2 shows an example of a flow through the system 100 of the present invention.

[0035] In this example, it is assumed that the device 50 described above with reference to Figures 1A to 1D is equipped with a scanner, and data representing the surface shape of the blade 11 is acquired when the device 50 is performing maintenance on the blade 11 of the wind turbine 10.

[0036] For example, suppose that the device 50 is performing maintenance on the blade 11 and finds a small damaged area that does not need to be repaired at this time. The small damaged area may appear in the data representing the surface shape of the blade 11.

[0037] In step S1, the device 50 provides data representing the surface shape of the blade 11 to the system 100. The manner in which the device 50 provides the data to the system 100 is not important. For example, the device 50 can transmit the data to the system 100 via wireless communication or wired communication. Alternatively, for example, the device 50 can provide the data to the system 100 via a storage medium (e.g., removable media).

[0038] In step S1, in addition to the data representing the surface shape of the blades 11, environmental data representing the environment around the wind turbine 10 (e.g., wind speed, rainfall, raindrops, UV radiation, air temperature, water temperature, air pressure, lightning strikes, location, etc. at the location where the wind turbine 10 is built) may also be provided to the system 100, and alternatively or in addition, material data representing the material of at least a portion of the blades 11 of the wind turbine 10 (e.g., putty or paint applied to the blades 11) may also be provided to the system 100.

[0039] When data representing the surface topography of the blades 11 (and, optionally, environmental data and / or material data) is provided to the system 100, the system 100 predicts, based on the provided data, when repairs should be made to the blades 11 of the wind turbine 10. Specifically, the system 100 predicts when repairs should be made to damaged portions indicated in the data representing the surface topography of the blades 11.

[0040] The system 100 can predict when repairs should be performed in any time unit. For example, the system 100 can predict when repairs should be performed in daily, weekly, biweekly, monthly, bimonthly, six-monthly, or yearly units. The system 100 can select an appropriate prediction model to perform predictions in the desired time unit.

[0041] The timing predicted by the system 100 is provided to the terminal device of the user U in step S2. The manner in which the system 100 provides the output to the terminal device of the user U is not important. For example, the system 100 can transmit the output to the terminal device of the user U via wireless communication or wired communication. Alternatively, for example, the system 100 can provide the output to the terminal device of the user U via a storage medium (e.g., removable media). The user U can be, for example, a maintenance company that performs maintenance on the wind turbine 10. Alternatively, the user U can be a business that operates the wind turbine 10.

[0042] This allows the user U to know when to repair small damaged areas that are found during maintenance of the blade 11 but do not currently require repair, and to repair the damaged areas at the appropriate time. This can, for example, eliminate the need for periodic inspections to determine whether damaged areas exist, leading to reduced maintenance costs.

[0043] Furthermore, since the system 100 predicts the timing for repairs based on objective data, even an inexperienced user U can understand when the damaged portion should be repaired.

[0044] In the above example, the device 50 measures the surface shape of the blade 11 while performing maintenance on the blade 11, but the present invention is not limited to this. For example, the surface shape of the blade may be measured by means other than the device 50. For example, a flying device such as a drone equipped with a measuring device such as a scanner may approach the blade 11 and measure the surface shape of the blade. In this case, the measurement data is provided to the system 100 from the flying device.

[0045] The above-described system 100 may have, for example, the configuration described below.

[0046] 2. System configuration for predicting when repairs should be performed FIG. 3 shows an example of the configuration of the system 100 of the present invention.

[0047] The system 100 comprises a receiving means 110 and a predicting means 120 .

[0048] The receiving means 110 is configured to receive data representing the surface shape of the wind turbine blades. The data representing the surface shape of the wind turbine blades is data measured by a measuring means. The measuring means may be, for example, a measuring means such as a scanner included in the above-mentioned device 50, or a measuring means such as a scanner included in a measuring device separate from the device 50.

[0049] The receiving means 110 may receive data representing the surface shape of the wind turbine blades from the device 50, or may receive the data from a measurement device separate from the device 50, for example.

[0050] The data representing the surface shape of a wind turbine blade may be data representing the external shape of a cross section of the wind turbine blade. The data representing the surface shape of a wind turbine blade may be, for example, point cloud data. The data representing the surface shape of a blade may also preferably represent the relative position of the surface shape of the blade with respect to the measuring means. For example, the data representing the surface shape of a blade may be expressed in terms of position coordinates on a two-dimensional plane with the position of the measuring means as the origin for each of multiple points constituting the surface shape in a specific cross section. This allows the data representing the surface shape of a blade in a specific cross section to be plotted or drawn on a two-dimensional plane, for example, as shown in FIG. 4A.

[0051] The data representing the surface shape of a wind turbine blade may represent the surface shape of the entire blade, or may represent the surface shape of a portion of the blade. Preferably, the data representing the surface shape of a wind turbine blade may represent the surface shape of at least a damaged portion of the blade. The damaged portion may be defined by its depth, width, and position. Thus, the data representing the surface shape of a wind turbine blade may include the depth, width, and / or position of the damaged portion, preferably the data representing the surface shape of a wind turbine blade may include the depth and width of the damaged portion, and more preferably the data representing the surface shape of a wind turbine blade may include the depth, width, and position of the damaged portion. Here, width refers to the dimension along or parallel to the surface of the wind turbine blade, and depth refers to the dimension perpendicular to the surface of the wind turbine blade.

[0052] In data representing the surface shape of a wind turbine blade, damaged portions can be identified by comparison with data representing an ideal surface shape obtained from specifications, etc. Alternatively, damaged portions can be identified by comparison with data representing an undamaged surface shape estimated from an undamaged portion, for example.

[0053] In addition to the data representative of the surface shape of the wind turbine blades, the receiving means 110 may further receive environmental data representative of the environment around the wind turbine and / or material data representative of the material of at least a part of the wind turbine blades.

[0054] Environmental data describing the environment around the wind turbine may be, for example, data describing the weather conditions around the wind turbine, including, but not limited to, wind speed data, rainfall data, raindrop data, UV data, air temperature data, water temperature data, barometric pressure data, and lightning data. Wind speed data may be the average wind speed or maximum wind speed within a predetermined period at the location where the wind turbine is located, and may be measured, for example, by an anemometer installed at or near the wind turbine. Rainfall data may be the average rainfall or maximum rainfall within a predetermined period at the location where the wind turbine is located, and may be measured, for example, by a rain gauge installed at or near the wind turbine. In this specification, the term "rain" may refer to not only the usual meaning of "rain" but also fog, hail, and snow. Raindrop data may represent the size of raindrops falling at the location where the wind turbine is located or the type of rain falling at the location where the wind turbine is located. The size of a raindrop refers to its diameter if the raindrop is spherical, or its maximum dimension if the raindrop is not spherical. For example, in the case of drizzle, the size of the raindrop is less than approximately 0.5 mm, while in the case of normal rain, the size of the raindrop is approximately 1.0 to 2.0 mm, and in some cases, it may exceed approximately 5.0 mm. Types of rain include, but are not limited to, heavy rain, light rain, drizzle, fog, hail, and snow. The raindrop data may be measured, for example, by a rain gauge installed at or near the wind turbine. The UV amount data may be the average or maximum amount of UV radiation within a specified period at the location where the wind turbine is located, and may be measured, for example, by a UV meter installed at or near the wind turbine. The temperature data may be the average, maximum, or minimum temperature within a specified period at the location where the wind turbine is located, and may be measured, for example, by a thermometer installed at or near the wind turbine. If the wind turbine is located offshore, the water temperature data may be the average water temperature, maximum water temperature, or minimum water temperature within a predetermined period at the location where the wind turbine is located, and may be measured, for example, by a thermometer installed at or near the wind turbine. The barometric pressure data may be the average barometric pressure, maximum barometric pressure, or minimum barometric pressure within a predetermined period at the location where the wind turbine is located, and may be measured, for example, by a barometer installed at or near the wind turbine. The lightning strike data may be the frequency of lightning strikes to the wind turbine, or the frequency of thunderclouds occurring within a predetermined period at the location where the wind turbine is located, and may be measured, for example, by a lightning rod or lightning receptor installed at the wind turbine.The data representing these weather conditions may be obtained from weather data published by organizations such as the Japan Meteorological Agency.

[0055] The environmental data representing the environment around the wind turbine may be, for example, data representing the location where the wind turbine is built (i.e., location). Data representing the location may include, but is not limited to, whether the location is near the sea (i.e., whether the wind turbine is located within the range of seawater sprays and the like that can be carried by the wind), whether the location is near sandy soil (i.e., whether the wind turbine is located within the range of sand and the like that can be carried by the wind), whether the location is near a forest (i.e., whether the wind turbine is located within the range of pollen and the like that can be carried by the wind), and whether the location is near a factory or power plant (i.e., whether the wind turbine is located within the range of soot and the like that can be carried by the wind). Such location data may be obtained, for example, from map data published by an organization such as the Geospatial Information Authority of Japan.

[0056] The environmental data representing the environment around the wind turbine preferably includes data representing the location. By taking into account the data representing the location, it is possible to make predictions that take into account the presence of physical factors that lead to deterioration of wind turbine blades (e.g., seawater spray, sand, pollen, soot, etc.), thereby improving the accuracy of the predictions. The environmental data representing the environment around the wind turbine preferably includes rainfall data in addition to, or instead of, the data representing the location. By taking into account the rainfall data, it is possible to make predictions that take into account the effects of rain that lead to deterioration of wind turbine blades, thereby improving the accuracy of the predictions. The environmental data representing the environment around the wind turbine preferably includes wind speed data, particularly average wind speed data, in addition to, or instead of, the data representing the location and / or the rainfall data. By taking into account the wind speed data, it is possible to make predictions that take into account the effects of wind that lead to deterioration of wind turbine blades, thereby improving the accuracy of the predictions. The environmental data representing the environment around the wind turbine preferably includes raindrop data in addition to, or instead of, the data representing the location, rainfall data, and / or wind speed data. By taking the raindrop data into account, it becomes possible to make predictions that appropriately take into account the erosive power of raindrops.

[0057] The material data representing the material of at least a part of the wind turbine blade includes, but is not limited to, for example, paint data representing the material of the paint on the surface of the blade, putty data representing the material of the putty on the surface of the blade, protective material data representing the material of the protective material (e.g., film, etc.) on the surface of the blade, and base material data representing the material of the base material of the blade. These material data may be obtained from the past maintenance history or from the specifications of the wind turbine.

[0058] The received data is passed to a prediction means 120 .

[0059] The prediction means 120 is configured to predict when repairs should be made to the blades of the wind turbine based at least on data describing the surface shape of the blades of the wind turbine.

[0060] The prediction means 120 can predict the timing for repairing the blades of the wind turbine by using, for example, a prediction model. The prediction model can be a trained model constructed by machine learning. The prediction model can be constructed using any machine learning model. The machine learning model can be, for example, a neural network model.

[0061] FIG. 4B shows an example of the structure of a neural network model for constructing a prediction model that can be used by the predictor 120.

[0062] The neural network model 600 has an input layer, a hidden layer, and an output layer. The number of nodes in the input layer of the neural network model 600 corresponds to the number of dimensions of the input data (e.g., the number of dimensions of the data received by the receiving means 110). The number of nodes in the output layer of the neural network model corresponds to the number of dimensions of the output data. The output value representing the timing for repairing the wind turbine blades may be, for example, one-dimensional. The hidden layer of the neural network may have any architecture. The hidden layer may have any number of layers, and each layer of the hidden layer may have any number of nodes.

[0063] The neural network model may be trained in advance using data representing previously measured blade surface shapes and values ​​indicating when previous repairs were performed on the blade. The training process is a process of calculating weight coefficients for each node in the intermediate layer of the neural network model using data representing the measured blade surface shapes and values ​​indicating the interval between when the blade was repaired and when it was measured.

[0064] The learning process may be, for example, supervised learning, which uses data representing the measured surface shape of a blade as input training data, values ​​representing the interval between the time when the blade was repaired and the time when it was measured as output training data, and calculates weight coefficients for each node in the intermediate layer of a neural network model using data from multiple blades or multiple repairs, thereby constructing a predictive model that can correlate data representing the measured surface shape of the blade with values ​​representing the time interval between measurement and repair.

[0065] In this case, by setting the time interval to a desired time unit, a prediction model capable of predicting timing in a desired time unit can be constructed. For example, if it is desired to predict timing in daily units, the time interval can be set to a daily unit. For example, if it is desired to predict timing in monthly units, the time interval can be set to a monthly unit.

[0066] For example, a set of (input training data, output training data) for supervised learning could be (data representing the surface shape measured from the first blade, a value indicating the interval between when the first blade was repaired and when it was measured), (data representing the surface shape measured from the second blade, a value indicating the interval between when the second blade was repaired and when it was measured), ... (data representing the surface shape measured from the i-th blade, a value indicating the interval between when the i-th blade was repaired and when it was measured), ... etc. When data representing the surface shape measured from the blade to be repaired is input to the input layer of a neural network model that has undergone learning processing in this way, a value indicating how often repairs should be performed on that blade after measurement is output to the output layer.

[0067] For example, if the receiving means 110 receives environmental data representing the environment around the wind turbine and / or material data representing the material of at least a part of the wind turbine blade in addition to the data representing the surface shape of the wind turbine blade, the predicting means 120 can predict the timing for repairing the wind turbine blade based on the environmental data and / or the material data. In this case, the used predictive model can be trained to be able to correlate the data representing the surface shape of the wind turbine blade, the environmental data and / or the material data, and a value representing the time interval between measurement and repair.

[0068] In this case, the set of (input training data, output training data) for supervised learning may be ((data representing the surface shape measured from the first blade, environmental data and / or material data of the first blade), a value indicating the interval between when the first blade was repaired and when the measurement was made), ((data representing the surface shape measured from the second blade, environmental data and / or material data of the second blade), a value indicating the interval between when the second blade was repaired and when the measurement was made), ... ((data representing the surface shape measured from the i-th blade, environmental data and / or material data of the i-th blade), a value indicating the interval between when the i-th blade was repaired and when the measurement was made), ..., etc. When data representing the surface shape measured from the blade to be repaired and the environmental data and / or material data of that blade are input to the input layer of the neural network model that has undergone learning processing in this way, a value indicating how often repairs should be made to that blade after measurement is output to the output layer.

[0069] In particular, it is preferable to use raindrop data as environmental data. For example, even with the same amount of rainfall, the rate at which damage to blades progresses due to rainfall may differ depending on the size of the raindrops or the type of rain. This is because the larger the size of the raindrops, the greater the erosive force they exert on the blades, and the smaller the size of the raindrops, the less erosive force they exert on the blades. Using raindrop data allows predictions to be made that appropriately take into account the erosive force of raindrops, resulting in more accurate predictions than when only the amount of rainfall is considered.

[0070] The prediction means 120 may identify damaged portions of the blades indicated in the data representing the surface shape of the blades, and use an appropriate prediction model according to the state of the damaged portions. That is, the prediction means 120 can select a prediction model based on the state of the damaged portions of the blades, and use the selected prediction model to predict the timing for repairing the wind turbine blades.

[0071] The aspect of the damaged portion includes, but is not limited to, for example, the erosion pattern of the damaged portion, which refers to the pattern in which the damaged portion expands due to erosion.

[0072] FIG. 5 shows two examples of erosion patterns. A cross-sectional view of blade 11 is shown in FIG. 5. In FIG. 5, (1) represents the first stage, (2) represents the second stage, (3) represents the third stage, and (4) represents the fourth stage. The erosion pattern on the left side of FIG. 5 shows the first erosion pattern, and the erosion pattern on the right side of FIG. 5 shows the second erosion pattern. In the first stage, shown as (1), a primary damage site P exists. The first and second erosion patterns can be distinguished according to how the erosion extends from the primary damage site P.

[0073] In the first erosion pattern, shown on the left side of Figure 5, the primary damage P expands in a direction perpendicular to the surface of the blade 11 (vertical direction in Figure 5), as shown at (3). The expanded damage has a depth greater than its width, typically being at least twice as deep as its width, and more typically being at least three times as deep as its width. In the first erosion pattern, erosion expands from the expanded damage in a direction parallel to the surface of the blade 11 (horizontal direction in Figure 5), as shown at (4). This results in the formation of a deep and wide damage region.

[0074] In the second erosion pattern shown on the right side of Figure 5, the primary damage P expands in a direction parallel to the surface of the blade 11 (left-right direction in Figure 5), as shown in (2). The expanded damage has a width greater than its depth, typically being at least twice as wide as its depth, and more typically being at least three times as wide as its depth. In the second erosion pattern, erosion forms a secondary damage in the expanded damage in a direction perpendicular to the surface of the blade (up-down direction in Figure 5), as shown in (3), and the secondary damage expands in a direction parallel to the surface of the blade 11 (left-right direction in Figure 5). This results in the formation of a deep and wide damage area.

[0075] These differences in erosion patterns result in differences in the type of damaged area formed, which in turn may result in differences in the timing for repair. By identifying these differences in erosion patterns and using an appropriate predictive model, it is possible to predict the timing for repair with greater accuracy. For example, in the first stage of (1), the loss of power generation efficiency of the wind turbine is, for example, -1%, and the urgency of repair is low. In the second stage of (2), the loss of power generation efficiency of the wind turbine is, for example, -2%, in the third stage of (3), the loss of power generation efficiency of the wind turbine is, for example, -3%, and in the fourth stage of (4), the loss of power generation efficiency of the wind turbine is, for example, -5%, and the urgency of repair gradually increases.

[0076] For example, the predictor 120 can compare the width and depth of the lesion extending from the primary lesion, and if depth > width, identify it as a first erosion pattern, and if width > depth, identify it as a second erosion pattern.

[0077] For example, the prediction means 120 selects a first prediction model for a damaged portion having a first erosion pattern and predicts the timing for repair using the first prediction model. The first prediction model is a trained model that has been trained using the training data described above for the damaged portion having the first erosion pattern.

[0078] For example, the prediction means 120 selects a first prediction model for a damaged portion having a second erosion pattern and predicts the timing for repair using the first prediction model. The first prediction model is a trained model that has been trained using the training data described above for the damaged portion having the first erosion pattern.

[0079] In the above example, only the first pattern and the second pattern are described, but the erosion patterns are not limited to these. In addition to these erosion patterns, other erosion patterns may be included.

[0080] For example, the prediction means 120 may compare the width and depth of a damaged portion (e.g., a damaged portion expanded from a primary damaged portion) and identify it as a first erosion pattern if depth > 2 × width, identify it as a second erosion pattern if width > 2 × depth, and identify the rest as a third erosion pattern. In this case, the prediction means 120 may select a third prediction model for a damaged portion having the third erosion pattern and predict the timing for repair using the third prediction model. The third prediction model is a trained model that has been trained using the training data described above for a damaged portion having the third erosion pattern.

[0081] The predicted timing may be output from the system 100 and provided, for example, to a technician performing maintenance on the blade so that the technician knows when to perform maintenance on the blade. For example, the system 100 may provide an alert (e.g., a visual or audible alert) to the technician when it is time to perform maintenance on the blade.

[0082] The above-described system 100 can be implemented as, for example, any information processing device. The information processing device may be, for example, a server device or a user terminal device (for example, a personal computer, a tablet, a smartphone, etc.).

[0083] FIG. 6 shows an example of the configuration of an information processing device 200 that implements the system 100.

[0084] The information processing device 200 includes an interface unit 210, a processor unit 220, and a memory unit 230. The information processing device 200 can be connected to a database unit 300.

[0085] The interface unit 210 exchanges information with the outside of the information processing device 200. The processor unit 220 of the information processing device 200 can receive information from the outside of the information processing device 200 and can transmit information to the outside of the information processing device 200 via the interface unit 210. The interface unit 210 can exchange information in any format. For example, the above-mentioned device 50 or a measurement device can communicate with the information processing device 200 via the interface unit 210. For example, if the information processing device 200 is a server device, a user's terminal device can communicate with the information processing device 200 via the interface unit 210.

[0086] The processor unit 220 executes the processing of the information processing device 200 and controls the overall operation of the information processing device 200. The processor unit 220 reads out a program stored in the memory unit 230 and executes the program. This allows the information processing device 200 to function as a system that executes desired steps. The processor unit 220 may be implemented by a single processor or by multiple processors.

[0087] The memory unit 230 stores programs required to execute the processing of the information processing device 200, data required to execute the programs, and the like. The memory unit 230 may store a program for causing the processor unit to execute processing for predicting the timing for repairing wind turbine blades (for example, a program for realizing the processing shown in FIGS. 7A and 7B described below). Here, how the program is stored in the memory unit 230 is not important. For example, the program may be pre-installed in the memory unit 230. Alternatively, the program may be installed in the memory unit 230 by being downloaded via a network. Alternatively, the program may be stored in a non-transitory computer-readable storage medium. The memory unit 230 may be implemented by any storage means.

[0088] For example, a prediction model used to predict when to repair wind turbine blades may be stored in the database unit 300. The processor unit 220 may, for example, select and acquire a prediction model stored in the database unit 300 and use the prediction model to predict the timing.

[0089] The database unit 300 may store training data for constructing a prediction model. The training data may include, for example, data representing the surface shape of a blade measured in the past and data indicating when the blade was repaired. In addition to these, the training data may also include environmental data and / or material data measured in the past.

[0090] The receiving means 110 of the system 100 may be implemented by an interface unit 210 and / or a processor unit 220. The predicting means 120 of the system 100 may be implemented by the processor unit 220.

[0091] In the example shown in FIG. 6 , the database unit 300 is provided externally to the information processing device 200, but the present invention is not limited to this. At least a portion of the database unit 300 can also be provided internally to the information processing device 200. In this case, at least a portion of the database unit 300 may be implemented by the same storage means as the storage means that implements the memory 230, or by a storage means different from the storage means that implements the memory unit 230. In either case, at least a portion of the database unit 300 is configured as a storage unit for the information processing device 200. The configuration of the database unit 300 is not limited to a specific hardware configuration. For example, the database unit 300 may be configured as a single hardware component or multiple hardware components. For example, the database unit 300 may be configured as an external hard disk drive for the information processing device 200, or as cloud storage connected via a network.

[0092] Each component of the information processing device 200 described above may be composed of a single hardware component or multiple hardware components. When composed of multiple hardware components, the manner in which the hardware components are connected does not matter. The hardware components may be connected wirelessly or by wire. It is also within the scope of the present invention that the processor unit 220 is configured using an analog circuit rather than a digital circuit. The system 100 is not limited to a specific hardware configuration. The configuration of the system 100 is not limited to the one described above as long as it can realize its functions.

[0093] 3. Processing in the system to predict when repairs should be performed 7A shows an example of a process 700 in the system 100 of the present invention. In this example, the system 100 is implemented by an information processing device 200. The process 700 is executed in the processor unit 220 of the information processing device 200.

[0094] In step S701, the processor unit 220 receives data representing the surface shape of the wind turbine blades. The processor unit 220 can receive data from outside the information processing device 200 via the interface unit 210. For example, the processor unit 220 can receive data via the interface unit 210 from a measuring means (for example, a measuring means provided in the device 50, or a measuring means provided in a device other than the device 50).

[0095] In addition to the data representing the surface shape of the wind turbine blades, the processor unit 220 may further receive environmental data representing the environment around the wind turbine and / or material data representing the material of at least a portion of the wind turbine blades. For example, the processor unit 220 can receive the environmental data from a measuring instrument installed on or near the wind turbine via the interface unit 210. For example, the processor unit 220 can receive the environmental data and / or material data from a publicly available database via the interface unit 210.

[0096] In step S702, the processor unit 220 predicts when to repair the wind turbine blades based on the data received in step S701. The processor unit 220 can predict when to repair the wind turbine blades using a prediction model. For example, the data received in step S701 can be input into a prediction model that can correlate data representing the surface shape of the blades with a value indicating the time interval between measurement and repair, thereby causing the prediction model to output the time to repair the blades.

[0097] In step S701, if environmental data and / or material data are received in addition to data representing the surface shape of the wind turbine blades, the processor unit 220 can input the data representing the surface shape of the blades and the environmental data and / or material data into the predictive model, thereby causing the predictive model to output the timing for blade repair.

[0098] The predicted timing is output to the outside of the information processing device 200. For example, it may be provided to a worker performing maintenance on the blade, allowing the worker to know when to perform blade repair. For example, the information processing device 200 may provide an alert (for example, a visual alert or an audible alert) to the worker when it is time to perform blade repair.

[0099] In step S702, as will be described later with reference to FIG. 7B, a prediction model may be selected according to the state of the damaged portion of the blade, and prediction may be performed using the selected prediction model.

[0100] FIG. 7B shows an example of the process of step S702 in process 700.

[0101] In step S7021, the processor unit 220 identifies the type of damaged portion on the surface of the blade from the data representing the surface shape of the wind turbine blade received in step S701.

[0102] The processor unit 220 first identifies damaged portions from the data representing the surface shape, and then identifies the characteristics (for example, depth and width, or shape, etc.) of the identified damaged portions.

[0103] In step S7022, processor unit 220 determines whether the erosion pattern of the damaged portion is the first pattern or the second pattern based on the aspect of the damaged portion identified in step S7021. If it is determined that the erosion pattern of the damaged portion is the first pattern, the process proceeds to step S7023. If it is determined that the erosion pattern of the damaged portion is the second pattern, the process proceeds to step S7024.

[0104] In step S7023, the processor unit 220 selects the first prediction model as the prediction model to be used. The first prediction model is a trained model that has been trained using training data obtained from a blade having a damaged portion with a first erosion pattern.

[0105] In step S7024, processor unit 220 selects the second prediction model as the prediction model to be used. The second prediction model is a trained model that has been trained using training data obtained from a blade having a damaged portion with the second erosion pattern.

[0106] In step S7025, the prediction model selected in step S7023 or step S7024 is used to predict the timing of repairs based on the data received in step S701.

[0107] In this way, by taking differences in erosion patterns into account when predicting the timing for repairs, it is possible to make predictions with greater accuracy.

[0108] 7A and 7B, the steps are described as being performed in a particular order. However, the order shown is merely an example, and the order in which the steps are performed is not limited to this. The steps may be performed in any order that is logically possible. Furthermore, other steps may be performed in addition to or instead of the steps shown.

[0109] In the example described above with reference to Figures 7A and 7B, the processing of each step shown in Figures 7A and 7B can be implemented by the processor unit 220 and a program stored in the memory unit 230 of the information processing device 200 that implements the system 100. However, the present invention is not limited to this. At least one of the processing of each step shown in Figures 7A and 7B may be implemented by a hardware configuration such as a control circuit.

[0110] The present invention is not limited to the above-described embodiments. It is understood that the scope of the present invention should be interpreted only by the claims. It is understood that a person skilled in the art can implement an equivalent scope based on the description of the present invention and common technical knowledge from the description of specific preferred embodiments of the present invention. [Industrial Applicability]

[0111] The present invention is useful for providing a system for predicting when repairs should be made to wind turbine blades. [Explanation of symbols]

[0112] 10 windmill 11, 12, 13 blades 14 Nacelle 15 Hub 20 Rope 50 equipment 100 systems 110 Receiving means 120 Prediction Methods

Claims

[Claim 1] The invention described in this specification.

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