Wind power system fault detection method, device, medium, equipment and wind power system

Abstract

The present disclosure relates to a wind power system fault detection method, device, medium, equipment and wind power system. The method comprises: determining the rotation speed of the plurality of wind turbines; determining the average rotation speed and the maximum rotation speed of the plurality of wind turbines; determining the number of wind turbines with rotation speed not less than the average rotation speed; determining whether the plurality of wind turbines has a fault according to the average rotation speed of the plurality of wind turbines, the maximum rotation speed of the plurality of wind turbines and the number of wind turbines with rotation speed not less than the average rotation speed. In this way, whether the plurality of wind turbines has a fault can be accurately and quickly determined, saving manual work.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of wind power operation and maintenance, in particular, to a wind power system fault detection method, device, medium, equipment and wind power system. BACKGROUND

[0002] Wind power is a clean energy and has been widely promoted in the northwest region of China, and is an indispensable part of the power system in China. However, wind turbines are exposed to the natural environment for a long time, and are often damaged due to natural factors, thereby affecting the power generation efficiency.

[0003] At present, wind turbines in a wind power system are widely laid and have a large quantity. In the related art, whether a wind turbine is faulty is usually determined by manual judgment, which is low in efficiency and accuracy. SUMMARY

[0004] The purpose of the present disclosure is to provide a wind power system fault detection method, device, medium, equipment and wind power system, which can accurately and quickly determine whether a wind turbine is faulty.

[0005] To achieve the above purpose, the present disclosure provides a wind power system fault detection method, the wind power system comprising a plurality of wind turbines, the method comprising:

[0006] determining the rotation speed of the plurality of wind turbines;

[0007] determining the average rotation speed and the maximum rotation speed of the plurality of wind turbines;

[0008] determining the number of wind turbines with rotation speed not less than the average rotation speed;

[0009] determining whether the plurality of wind turbines is faulty according to the average rotation speed of the plurality of wind turbines, the maximum rotation speed of the plurality of wind turbines and the number of wind turbines with rotation speed not less than the average rotation speed.

[0010] Optionally, the determining whether the plurality of wind turbines is faulty according to the average rotation speed of the plurality of wind turbines, the maximum rotation speed of the plurality of wind turbines and the number of wind turbines with rotation speed not less than the average rotation speed comprises:

[0011] calculating a rotation speed threshold ω0 according to the following formula:

[0012]

[0013] wherein, ω0 represents the average rotation speed of the plurality of wind turbines, ωmax represents the maximum rotation speed of the plurality of wind turbines, m represents the number of the plurality of wind turbines, and n represents the number of wind turbines with rotation speed not less than the average rotation speed. max ω0 represents the average rotation speed of the plurality of wind turbines, ωmax represents the maximum rotation speed of the plurality of wind turbines, m represents the number of the plurality of wind turbines, and n represents the number of wind turbines with rotation speed not less than the average rotation speed.

[0014] The fan with the rotation speed less than the rotation speed threshold is determined as the faulty fan.

[0015] Optionally, the method further comprises:

[0016] Obtaining a blade image of the faulty fan;

[0017] From the predetermined plurality of blade images, determining an image with the highest similarity to the blade image of the faulty fan as a selected image;

[0018] Looking up a fault type corresponding to the selected image in a predetermined correspondence relationship as the determined fault type, the correspondence relationship including a correspondence relationship between the predetermined plurality of blade images and fault types.

[0019] Optionally, the wind power system includes a UAV inspection team, and the UAV inspection team includes a general-purpose UAV and a fault detection UAV.

[0020] The determining of the rotation speeds of the plurality of fans includes: receiving images of the plurality of fans sent by the general-purpose UAV; and determining the rotation speeds of the plurality of fans according to the images of the plurality of fans.

[0021] The obtaining of the blade image of the faulty fan includes: receiving the blade image of the faulty fan sent by the fault detection UAV.

[0022] The method further comprises:

[0023] Obtaining a wind direction and a wind force of an environment currently located by the UAV inspection team;

[0024] Adjusting flight parameters of the UAV inspection team according to the following formula:

[0025]

[0026]

[0027] Wherein, α represents an included angle between the flight direction before adjustment and the due east direction, β represents an included angle between the flight direction after adjustment and the due east direction, θ represents an included angle between the wind direction and the normal line of the flight direction before adjustment, v represents the size of the flight speed of the UAV inspection team, F represents the size of the wind force, k represents a wind force influence coefficient, t represents the flight time after adjustment, and t' represents the flight time before adjustment.

[0028] Optionally, the method further comprises:

[0029] Obtaining a distance between a projection of the fault detection UAV on a rotating plane of the blade of the faulty fan and a rotating center and a radial moving distance of the fault detection UAV relative to the blade of the faulty fan in a unit time.

[0030] A first target speed v1 of the fault detection UAV when moving from the inside to the outside of the fault wind turbine blade is calculated according to the following formula:

[0031]

[0032] Wherein, Δd is the radial movement distance of the fault detection UAV relative to the fault wind turbine blade per unit time, ω represents the rotating speed of the fault wind turbine, and d represents the distance between the projection of the fault detection UAV on the rotating plane of the fault wind turbine blade and the rotating center.

[0033] The fault detection UAV is controlled to fly at the first target speed when moving from the inside to the outside of the fault wind turbine blade.

[0034] Optionally, the method further comprises:

[0035] A second target speed v2 of the fault detection UAV when moving from the outside to the inside of the fault wind turbine blade is calculated according to the following formula:

[0036]

[0037] The fault detection UAV is controlled to fly at the second target speed when moving from the outside to the inside of the fault wind turbine blade.

[0038] The present disclosure also provides a wind power system fault detection device, the wind power system comprising a plurality of wind turbines, the device comprising:

[0039] A first determining module for determining the rotating speeds of the plurality of wind turbines;

[0040] A second determining module for determining the average value and the maximum value of the rotating speeds of the plurality of wind turbines;

[0041] A third determining module for determining the number of wind turbines whose rotating speeds are not less than the average value of the rotating speeds;

[0042] A fourth determining module for determining whether the plurality of wind turbines have faults according to the average value of the rotating speeds of the plurality of wind turbines, the maximum value of the rotating speeds of the plurality of wind turbines, and the number of wind turbines whose rotating speeds are not less than the average value of the rotating speeds.

[0043] The present disclosure also provides a non-transitory computer readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps of the wind power system fault detection method of the claims.

[0044] The present disclosure also provides an electronic device comprising:

[0045] A memory having a computer program stored thereon;

[0046] a processor configured to execute the computer program in the memory to implement the steps of the wind power system fault detection method.

[0047] The present disclosure also provides a wind power system, comprising:

[0048] a plurality of wind turbines;

[0049] the electronic device described above;

[0050] a UAV inspection team, comprising:

[0051] a wind detection UAV configured to detect the wind direction and wind force of the environment;

[0052] a panoramic image UAV configured to collect images of the plurality of wind turbines;

[0053] a fault detection UAV configured to collect images of the blades of the fault wind turbine;

[0054] a safety control UAV configured to determine the distance between the projection of the fault detection UAV on the rotation plane of the blades of the fault wind turbine and the rotation center and the radial movement distance of the fault detection UAV relative to the blades of the fault wind turbine per unit time.

[0055] By the above technical solution, the rotation speeds of the plurality of wind turbines are determined, the average rotation speed and the maximum rotation speed of the plurality of wind turbines are determined, the number of wind turbines with rotation speeds not less than the average rotation speed is determined, and whether the plurality of wind turbines have faults is determined according to the average rotation speed of the plurality of wind turbines, the maximum rotation speed of the plurality of wind turbines, and the number of wind turbines with rotation speeds not less than the average rotation speed. In this way, whether the plurality of wind turbines have faults can be accurately and quickly determined, and manual work is saved.

[0056] Other features and advantages of the present disclosure will be described in detail in the following detailed description section. BRIEF DESCRIPTION OF DRAWINGS

[0057] The accompanying drawings are included to provide a further understanding of the present disclosure and constitute a part of the specification, which together with the following detailed description, serve to explain the present disclosure. In the drawings:

[0058] Figure 1 is a flowchart of the wind power system fault detection method provided by an exemplary embodiment.

[0059] Figure 2 is a schematic diagram of the flight parameter adjustment process of the UAV inspection team provided by an exemplary embodiment.

[0060] Figure 3FIG. 1 is a schematic diagram of a fault detection UAV moving from the outside to the inside of a fault wind turbine blade according to an example embodiment.

[0061] Figure 4 FIG. 2 is a schematic diagram of a fault detection UAV moving from the inside to the inside of a fault wind turbine blade according to an example embodiment.

[0062] Figure 5 FIG. 3 is a block diagram of a wind power system fault detection device according to an example embodiment. DETAILED DESCRIPTION

[0063] The specific embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure, and are not used to limit the present disclosure.

[0064] It should be noted that all actions of obtaining signals, information or data in the present disclosure are carried out in accordance with the corresponding data protection regulations and policies of the country where the device is located, and with the authorization of the corresponding device owner.

[0065] Figure 1 FIG. 4 is a flowchart of a wind power system fault detection method according to an example embodiment. As shown in FIG. 4, the wind power system includes a plurality of wind turbines, and the method includes the following steps. Figure 1

[0066] In step S101, the rotational speeds of the plurality of wind turbines are determined.

[0067] A plurality of wind turbines are usually arranged in each power generation area of the wind power system. The rotational speed of the wind turbine has a positive correlation with the power generation capacity, and the faster the rotational speed of the wind turbine, the stronger the power generation capacity. The rotational speed of the wind turbine is related to the wind speed of the power generation area where the wind turbine is arranged, and the faster the wind speed, the faster the rotational speed of the wind turbine. The rotational speeds of the plurality of wind turbines can be determined by methods in the related art.

[0068] In step S102, the average rotational speed and the maximum rotational speed of the plurality of wind turbines are determined.

[0069] After obtaining the rotational speeds of the plurality of wind turbines, the average rotational speed and the maximum rotational speed of the plurality of wind turbines can be determined by methods in the related art.

[0070] In step S103, the number of wind turbines with a rotational speed not less than the average rotational speed is determined.

[0071] After the average rotational speed is determined, the number of wind turbines with a rotational speed not less than the average rotational speed can be determined by methods in the related art.

[0072] ​In step S104, whether the plurality of wind turbines has a fault is determined according to the average value of the rotation speeds of the plurality of wind turbines, the maximum value of the rotation speeds of the plurality of wind turbines, and the number of wind turbines whose rotation speed is not less than the average value of the rotation speeds.

[0073] The plurality of wind turbines are arranged in the same power generation area, the wind speed at the position of each wind turbine is close to the same, and in the case that the plurality of wind turbines are of the same type, if none of the plurality of wind turbines has a fault, the rotation speeds of the plurality of wind turbines are close to the same. If a wind turbine has a fault, the rotation speed of the fault wind turbine and the rotation speed of the wind turbine without a fault can be significantly different. After the rotation speed of the wind turbine, the average value of the rotation speeds of the plurality of wind turbines, the maximum value of the rotation speeds of the plurality of wind turbines, and the number of wind turbines whose rotation speed is not less than the average value of the rotation speeds are determined, whether the plurality of wind turbines has a fault is determined according to the average value of the rotation speeds of the plurality of wind turbines, the maximum value of the rotation speeds of the plurality of wind turbines, and the number of wind turbines whose rotation speed is not less than the average value of the rotation speeds.

[0074] By the above technical solution, the rotation speeds of the plurality of wind turbines are determined, the average value and the maximum value of the rotation speeds of the plurality of wind turbines are determined, the number of wind turbines whose rotation speed is not less than the average value of the rotation speeds is determined, and whether the plurality of wind turbines has a fault is determined according to the average value of the rotation speeds of the plurality of wind turbines, the maximum value of the rotation speeds of the plurality of wind turbines, and the number of wind turbines whose rotation speed is not less than the average value of the rotation speeds. In this way, whether the plurality of wind turbines has a fault can be accurately and quickly determined, and manual work is saved.

[0075] In another embodiment, the above determining whether the plurality of wind turbines has a fault according to the average value of the rotation speeds of the plurality of wind turbines, the maximum value of the rotation speeds of the plurality of wind turbines, and the number of wind turbines whose rotation speed is not less than the average value of the rotation speeds includes:

[0076] The rotation speed threshold ω0 is calculated according to the following formula:

[0077]

[0078] wherein ω represents the average value of the rotation speeds of the plurality of wind turbines, ω max represents the maximum value of the rotation speeds of the plurality of wind turbines, m represents the number of the plurality of wind turbines, and n represents the number of wind turbines whose rotation speed is not less than the average value of the rotation speeds;

[0079] The wind turbine whose rotation speed is less than the rotation speed threshold is determined as a fault wind turbine.

[0080] The rotation speed threshold represents the minimum rotation speed of the plurality of wind turbines of the same type under normal working conditions without a fault at the same wind speed, and the rotation speed threshold ω0 can be calculated according to formula (1).

[0081] When the rotation speed of the wind turbine is less than the rotation speed threshold, the rotation speed of the wind turbine is low, and is obviously different from the rotation speed of the other wind turbines arranged in the same area, so it can be determined that the wind turbine has a fault. When the rotation speed of the wind turbine is not less than the rotation speed threshold, the rotation speed of the wind turbine is normal, so it can be determined that the wind turbine does not have a fault.

[0082] In this embodiment, the rotating speed threshold ω0 can be quickly calculated according to the above formula (1), and it can be determined whether the multiple fans are malfunctioning according to the threshold comparison result, which is simple and fast in data processing.

[0083] In another embodiment, the above method further comprises:

[0084] obtaining a blade image of the malfunctioning fan;

[0085] determining, from the predetermined multiple blade images, an image with the highest similarity to the blade image of the malfunctioning fan as a selected image;

[0086] finding, in the predetermined correspondence relationship, a malfunction type corresponding to the selected image as the determined malfunction type, the correspondence relationship including a correspondence relationship between the predetermined multiple blade images and the malfunction types.

[0087] The malfunction types of the fan include lightning damage, blade icing, leading edge corrosion, blade tip drain hole blockage, edge cracking, coating peeling, and bolt fracture. After the fan has the above malfunctions, the shape of the blade of the fan will change, so after the malfunctioning fan is determined, the blade image of the malfunctioning fan can be obtained.

[0088] Due to environmental factors and the severity of the malfunction, the blade images of the fans with different malfunction types are not the same, and even if the fan has the same type of malfunction (for example, blade icing), the blade images of the malfunctioning fans are not completely the same, so the blade images (standard images) of multiple malfunction types can be stored in advance, and the image with the highest similarity to the obtained blade image of the malfunctioning fan from the predetermined multiple blade images is determined as the selected image. The similarity can be calculated using methods known to those skilled in the art, and the specific principles are not described here.

[0089] After the selected image is determined, the malfunction type corresponding to the selected image can be found in the predetermined correspondence relationship as the determined malfunction type. For example, if the malfunction type corresponding to the selected image is coating peeling, the malfunction type is determined as coating peeling.

[0090] In this embodiment, the image with the highest similarity to the blade image of the malfunctioning fan is determined as the selected image, and the malfunction type corresponding to the selected image can be quickly determined in a table lookup manner, which is simple and fast in data processing.

[0091] In yet another embodiment, the wind power system comprises a UAV inspection module, an expert knowledge base module, and an inspection data analysis module. The UAV inspection module is configured to dispatch UAVs for operation and maintenance inspection according to inspection scheme data of the expert knowledge base module. The expert knowledge base module is configured to store the inspection scheme data, which comprises inspection time data, inspection area data, wind turbine inspection process data, and inspection team composition data. The inspection time data is used to control the time when the UAV starts inspection, the inspection area data is used to determine the area where the wind turbine to be inspected is located, and the inspection area data is also provided with flight parameters for the UAV to reach the corresponding inspection area from the take-off point. After the UAV reaches the inspection area, the wind turbine is inspected according to the wind turbine inspection process data. The inspection team composition data includes the number of UAVs required and the corresponding UAV types, and the UAV inspection module dispatches the corresponding UAVs according to the inspection team composition data. The inspection data analysis module is configured to analyze the data obtained by the UAV during the inspection to obtain the operation status of the wind turbine.

[0092] In yet another embodiment, the wind power system comprises a UAV inspection team, which comprises a general planning UAV and a fault detection UAV.

[0093] The above-mentioned determination of the rotational speed of the plurality of wind turbines comprises receiving images of the plurality of wind turbines sent by the general planning UAV, and determining the rotational speed of the plurality of wind turbines according to the images of the plurality of wind turbines.

[0094] The above-mentioned acquisition of the blade image of the fault wind turbine comprises receiving the blade image of the fault wind turbine sent by the fault detection UAV.

[0095] The above-mentioned method further comprises:

[0096] Acquiring the wind direction and wind force of the current environment in which the UAV inspection team is located;

[0097] Adjusting the flight parameters of the UAV inspection team according to the following formula:

[0098]

[0099]

[0100] wherein, α represents the included angle between the flight direction before adjustment and the due east direction, β represents the included angle between the flight direction after adjustment and the due east direction, θ represents the included angle between the wind direction and the normal line of the flight direction before adjustment, v represents the size of the flight speed of the UAV inspection team, F represents the size of the wind force, k represents the wind force influence coefficient, t represents the flight time after adjustment, and t' represents the flight time before adjustment. The flight time refers to the time for the UAV inspection team to fly to the corresponding inspection area.

[0101] The UAV inspection module of the wind power system comprises a UAV inspection team, which can comprise a panoramic UAV, a fault detection UAV. The panoramic UAV is used to take images of multiple wind turbines in the entire inspection area. After the panoramic UAV takes images of multiple wind turbines, the electronic device on the ground can receive the images of multiple wind turbines sent by the panoramic UAV, and determine the wind speed of multiple wind turbines according to the images of multiple wind turbines by using methods known to those skilled in the art. The fault detection UAV is used to take images of the blades of a fault wind turbine. For example, the electronic device can receive the images of the blades of the fault wind turbine taken by the fault detection UAV to obtain the images of the blades of the fault wind turbine.

[0102] The UAV inspection team further comprises a safety control UAV, which is used to control the movement of the fault detection UAV during the process of taking images of the blades of the fault wind turbine, so as to prevent the fault detection UAV from colliding with the blades of the fault wind turbine.

[0103] The UAV inspection team further comprises a wind detection UAV, which is used to obtain the wind direction and wind force of the current environment of the UAV inspection team.

[0104] The inspection process of the UAV inspection team comprises the following steps:

[0105] S1, the UAV inspection module receives one or more inspection scheme data sent by the expert knowledge base module;

[0106] S2, when the time point reaches the inspection time data in one of the inspection scheme data, the UAV inspection module calls the corresponding UAV according to the inspection team composition data in the corresponding inspection scheme data to form a UAV inspection team;

[0107] S3, the UAV inspection team flies to the corresponding inspection area according to the flight parameters in the inspection area data;

[0108] S4, the UAV inspection team performs inspection work on the wind turbine according to the wind turbine inspection process data;

[0109] S5, the UAV inspection team sends the collected data to the inspection data analysis module;

[0110] S6, the UAV inspection team performs subsequent work according to the feedback instructions of the inspection data analysis module;

[0111] S7, the UAV inspection team returns according to the flight parameters after completing the inspection.

[0112] Figure 2 is a schematic diagram of the flight parameter adjustment process of the UAV inspection team provided by an exemplary embodiment. As shown in Figure 2As shown, in the process of flying to the corresponding inspection area, the flight direction of the UAV inspection team (β represents the included angle between the adjusted flight direction and the due east direction) may deviate from the preset direction (α represents the included angle between the actual flight direction (flight direction before adjustment) and the due east direction) under the influence of wind speed and wind force. Therefore, the wind direction and wind force of the current environment of the UAV inspection team can be obtained, and the flight parameters of the UAV inspection team are adjusted according to the above formulas (2), (3), and the actual flight direction of the adjusted flight direction under the action of wind force and wind direction is equivalent to the target flight direction (the flight direction of the inspection area). The value of k in the above formula (2) can be preset by the designer according to the test. F·k represents the offset distance of the UAV under the action of F wind force per unit time.

[0113] In this embodiment, considering the influence of the wind direction and wind force of the current environment of the UAV inspection team on the flight parameters of the UAV inspection team, the flight parameters of the UAV inspection team can be quickly adjusted according to the above formulas (2), (3), and the inspection efficiency is improved.

[0114] In yet another embodiment, the working process of the UAV inspection team after flying to the inspection area includes the following steps:

[0115] S31, the overall picture UAV shoots images of multiple wind machines and sends them to the inspection data analysis module, and the inspection data analysis module returns fault wind machine data;

[0116] S32, the fault detection UAV and the safety control UAV fly to the vicinity of the corresponding fault wind machine;

[0117] S33, the fault detection UAV performs a spiral-like motion (relative to the ground) under the assistance of the safety control UAV, the spiral-like motion is composed of multiple straight line motions (relative to the wind machine blade), the fault detection UAV moves radially relative to the wind machine blade, shoots images of the blades of the fault wind machine, and sends them to the inspection data analysis module;

[0118] S34, after the fault detection UAV shoots images of the blades of all fault wind machines in step S32, the wind detection UAV leads the entire UAV inspection team to fly back to the take-off point.

[0119] In yet another embodiment, the above method further includes:

[0120] Obtaining the distance between the projection of the fault detection UAV on the rotation plane of the fault wind machine blade and the rotation center and the radial movement distance of the fault detection UAV relative to the fault wind machine blade per unit time;

[0121] The first target speed v1 of the fault detection UAV when moving from the inside to the outside of the fault wind machine blade is calculated according to the following formula:

[0122]

[0123] wherein, Δd represents the radial moving distance of the fault detection UAV relative to the fault wind turbine blade in unit time, ω represents the rotating speed of the fault wind turbine, and d represents the distance between the projection of the fault detection UAV on the rotating plane of the fault wind turbine blade and the rotating center;

[0124] The fault detection UAV flies at the first target speed when moving from the inside to the outside of the fault wind turbine blade.

[0125] In the process that the fault detection UAV shoots the blade image of the fault wind turbine, the fault detection UAV performs a helix-like motion under the assistance of the safety control UAV, the helix-like motion is composed of multiple straight line motions, and the fault detection UAV moves radially and linearly relative to the wind turbine blade.

[0126] The process that the fault detection UAV moves from the inside to the outside of the fault wind turbine blade includes the following steps:

[0127] S21, the fault detection UAV flies to the front of the rotating center of the fault wind turbine blade, adjusts the orientation and determines a moving plane, and the moving plane is parallel to the rotating plane of the blade;

[0128] S22, the fault detection UAV receives the rotating speed ω of the fault wind turbine and the distance d between the projection of the fault detection UAV on the rotating plane of the fault wind turbine blade and the rotating center sent by the safety control UAV;

[0129] S23, the fault detection UAV performs an outward helix-like motion at the first target speed v1, and the outward helix-like motion is composed of multiple straight line motions. Figure 3 is a schematic diagram of the fault detection UAV moving from the outside to the inside of the fault wind turbine blade provided by an example embodiment. In combination with Figure 3 According to the above formula (4), the first target speed v1 of the fault detection UAV when moving from the inside to the outside of the fault wind turbine blade can be calculated.

[0130] In this embodiment, according to the above formula (4), the first target speed v1 of the fault detection UAV when moving from the inside to the outside of the fault wind turbine blade can be quickly calculated, the method is simple, and the data processing speed is fast.

[0131] In yet another embodiment, the above method further includes:

[0132] According to the following formula, the second target speed v2 of the fault detection UAV when moving from the outside to the inside of the fault wind turbine blade can be calculated:

[0133]

[0134] The control fault detection UAV flies at the second target speed when moving from the outer side to the inner side of the fault wind turbine blade.

[0135] The process of the fault detection UAV moving from the outer side to the inner side of the fault wind turbine blade comprises the following steps:

[0136] S24, after moving to the outer end of the blade, the fault detection UAV hovers and waits for the next blade to rotate to the front;

[0137] S25, the fault detection UAV performs an inward spiral movement at the second target speed until it moves to the front of the center of the blade rotation, and the inward spiral movement is composed of several straight line movements. Figure 4 is a schematic diagram of the fault detection UAV moving from the outer side to the inner side of the fault wind turbine blade provided by an exemplary embodiment. In combination with Figure 4 The second target speed v2 of the fault detection UAV moving from the outer side to the inner side of the fault wind turbine blade can be calculated according to the above formula (5);

[0138] S26, repeat steps S23 to S25 until the same side of all blades is checked;

[0139] S27, the fault detection UAV moves to the other side of the wind turbine under the command of the safety control UAV, and repeats steps S23 to S26;

[0140] In steps S23 and S25, the closer the fault detection UAV is to the center of rotation, the larger the Δd is, and the faster the radial movement speed is; the farther the fault detection UAV is from the center of rotation, the smaller the Δd is, and the slower the radial movement speed is.

[0141] In this embodiment, the second target speed v2 of the fault detection UAV moving from the outer side to the inner side of the fault wind turbine blade can be quickly calculated according to the above formula (5), the method is simple, and the data processing speed is fast.

[0142] Based on the same inventive concept, the disclosure also provides a wind power system fault detection device. Figure 5 is a block diagram of a wind power system fault detection device provided by an exemplary embodiment. The wind power system comprises a plurality of wind turbines. As Figure 5 shown, the wind power system fault detection device 200 comprises a first determination module 201, a second determination module 202, a third determination module 203, and a fourth determination module 204.

[0143] The first determination module 201 is configured to determine the rotation speed of the plurality of wind turbines.

[0144] The second determination module 202 is configured to determine the average value and the maximum value of the rotation speed of the plurality of wind turbines.

[0145] The third determining module 203 is configured to determine the number of the wind turbines whose rotation speeds are not less than the average rotation speed.

[0146] The fourth determining module 204 is configured to determine whether the plurality of wind turbines are faulty according to the average rotation speed of the plurality of wind turbines, the maximum rotation speed of the plurality of wind turbines, and the number of the wind turbines whose rotation speeds are not less than the average rotation speed.

[0147] Optionally, the fourth determining module 204 comprises a calculating sub-module and a first determining sub-module.

[0148] The calculating sub-module is configured to calculate the rotation speed threshold ω0 according to the following formula:

[0149]

[0150] wherein, ω represents the average rotation speed of the plurality of wind turbines, ω max represents the maximum rotation speed of the plurality of wind turbines, m represents the number of the plurality of wind turbines, and n represents the number of the wind turbines whose rotation speeds are not less than the average rotation speed.

[0151] The first determining sub-module is configured to determine the wind turbine whose rotation speed is less than the rotation speed threshold as a faulty wind turbine.

[0152] Optionally, the wind power system fault detection apparatus 200 further comprises a first obtaining module, a fifth determining module, and a searching module.

[0153] The first obtaining module is configured to obtain the blade image of the faulty wind turbine.

[0154] The fifth determining module is configured to determine, from the plurality of predetermined blade images, the image with the highest similarity to the blade image of the faulty wind turbine as a selected image.

[0155] The searching module is configured to search, in a predetermined corresponding relationship, the fault type corresponding to the selected image as a determined fault type, the corresponding relationship comprising the corresponding relationship between the plurality of predetermined blade images and the fault types.

[0156] Optionally, the wind power system comprises a UAV inspection team, the UAV inspection team comprises a general planning UAV and a fault detection UAV, and the first determining module 201 comprises a first receiving sub-module and a second determining sub-module.

[0157] The first receiving sub-module is configured to receive the images of the plurality of wind turbines sent by the general planning UAV.

[0158] The second determining sub-module is configured to determine the rotation speeds of the plurality of wind turbines according to the images of the plurality of wind turbines.

[0159] The first obtaining module comprises a second receiving sub-module.

[0160] The second receiving sub-module is configured to receive the blade image of the faulty wind turbine sent by the fault detection UAV.

[0161] The wind power system fault detection device 200 further comprises a second obtaining module and an adjusting module.

[0162] The second obtaining module is configured to obtain the wind direction and wind force of the environment in which the UAV inspection team is currently located.

[0163] The adjusting module is configured to adjust the flight parameters of the UAV inspection team according to the following formula:

[0164]

[0165]

[0166] wherein, α represents the included angle between the flight direction before adjustment and the due east direction, β represents the included angle between the flight direction after adjustment and the due east direction, θ represents the included angle between the wind direction and the normal line of the flight direction before adjustment, v represents the size of the flight speed of the UAV inspection team, F represents the wind force, k represents the wind force influence coefficient, t represents the flight time after adjustment, and t' represents the flight time before adjustment.

[0167] Optionally, the wind power system fault detection device 200 further comprises a third obtaining module, a first calculating module and a first control module.

[0168] The third obtaining module is configured to obtain the distance between the projection of the fault detection UAV on the rotating plane of the faulty wind turbine blade and the rotating center and the radial movement distance of the fault detection UAV relative to the faulty wind turbine blade per unit time.

[0169] The first calculating module is configured to calculate the first target speed v1 of the fault detection UAV when moving from the inside to the outside of the faulty wind turbine blade according to the following formula:

[0170]

[0171] wherein, Δd represents the radial movement distance of the fault detection UAV relative to the faulty wind turbine blade per unit time, ω represents the rotating speed of the faulty wind turbine, and d represents the distance between the projection of the fault detection UAV on the rotating plane of the faulty wind turbine blade and the rotating center.

[0172] The first control module is configured to control the fault detection UAV to fly at the first target speed when moving from the inside to the outside of the faulty wind turbine blade.

[0173] Optionally, the wind power system fault detection device 200 further comprises a second calculating module and a second control module.

[0174] The second computing module is configured to calculate a second target speed v2 of the fault detection UAV when moving from the outside to the inside of the fault wind turbine blade according to the following formula:

[0175]

[0176] The second control module is configured to control the fault detection UAV to fly at the second target speed when moving from the outside to the inside of the fault wind turbine blade.

[0177] As to the device in the above-mentioned embodiments, the specific manner in which each module performs the operation has been described in detail in the embodiments of the method, and will not be described in detail here.

[0178] Through the above technical solution, the rotation speeds of the plurality of wind turbines are determined, the average value and the maximum value of the rotation speeds of the plurality of wind turbines are determined, the number of wind turbines with rotation speeds not less than the average value is determined, and whether the plurality of wind turbines has a fault is determined according to the average value of the rotation speeds of the plurality of wind turbines, the maximum value of the rotation speeds of the plurality of wind turbines, and the number of wind turbines with rotation speeds not less than the average value. In this way, whether the plurality of wind turbines has a fault can be accurately and quickly determined, and manual work is saved.

[0179] The present disclosure also provides a non-transitory computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps of the wind power system fault detection method described above.

[0180] The present disclosure also provides an electronic device, comprising:

[0181] a memory having a computer program stored thereon;

[0182] a processor configured to execute the computer program in the memory to implement the steps of the wind power system fault detection method described above.

[0183] The present disclosure also provides a wind power system, comprising:

[0184] a plurality of wind turbines;

[0185] the electronic device described above;

[0186] a UAV inspection team, comprising:

[0187] a wind detection UAV configured to detect the wind direction and wind force of the environment;

[0188] a general planning screen UAV configured to collect images of the plurality of wind turbines;

[0189] a fault detection UAV configured to collect images of the blades of the fault wind turbine;

[0190] The safety control unmanned aerial vehicle is used for determining the distance between the projection of the fault detection unmanned aerial vehicle on the rotation plane of the fault wind turbine blade and the rotation center and the radial moving distance of the fault detection unmanned aerial vehicle relative to the fault wind turbine blade in unit time.

[0191] The preferred embodiments of the present disclosure are described in detail above with reference to the drawings, but the present disclosure is not limited to the specific details in the above-described embodiments. Various simple modifications can be made to the technical solutions of the present disclosure within the technical concept of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.

[0192] In addition, it should be noted that each specific technical feature described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, various possible combinations are not described again in the present disclosure.

[0193] In addition, any combination of various different embodiments of the present disclosure can also be made as long as it does not deviate from the idea of the present disclosure, and it should also be considered as disclosed in the present disclosure.

Claims

1. A method for fault detection in a wind power system, characterized in that, The wind power system includes multiple wind turbines, and the method includes: Determine the rotational speed of the plurality of fans; Determine the average and maximum rotational speeds of the plurality of fans; Determine the number of fans whose rotational speed is not less than the average rotational speed; Based on the average rotational speed of the plurality of fans, the maximum rotational speed of the plurality of fans, and the number of fans with a rotational speed not less than the average rotational speed, it is determined whether the plurality of fans have malfunctioned; The step of determining whether the plurality of fans have malfunctioned based on the average rotational speed of the plurality of fans, the maximum rotational speed of the plurality of fans, and the number of fans whose rotational speed is not less than the average rotational speed includes: Calculate the speed threshold using the following formula. : in, This represents the average rotational speed of the multiple fans. The value of the rotational speed of the plurality of fans is represented by m, the number of the plurality of fans is represented by n, and the number of fans whose rotational speed is not less than the average rotational speed is represented by the rotational speed threshold, which characterizes the lowest rotational speed of the plurality of fans when no failure occurs at the same wind speed. Fans with a rotational speed lower than the specified speed threshold are identified as faulty fans.

2. The method according to claim 1, characterized in that, The method further includes: Obtain images of the blades of the faulty wind turbine; From a predetermined number of blade images, the image with the highest similarity to the blade image of the faulty wind turbine is selected as the chosen image; The fault type corresponding to the selected image is found in the predetermined correspondence, and the determined fault type is obtained. The correspondence includes the correspondence between the predetermined multiple blade images and fault types.

3. The method according to claim 2, characterized in that, The wind power system includes a drone inspection team, which includes a drone for coordinating video feeds and a drone for fault detection. Determining the rotational speed of the plurality of wind turbines includes: receiving images of the plurality of wind turbines sent by the unified view drone; and determining the rotational speed of the plurality of wind turbines based on the images of the plurality of wind turbines. The step of acquiring the blade image of the faulty wind turbine includes: receiving the blade image of the faulty wind turbine sent by the fault detection drone; The method further includes: Obtain the wind direction and wind force of the current environment in which the drone inspection team is located; The flight parameters of the drone inspection team should be adjusted according to the following formula: in, This indicates the angle between the original flight direction and the due east direction. This indicates the angle between the adjusted flight direction and the due east direction. The angle between the wind direction and the normal to the original flight direction is given; v represents the flight speed of the UAV inspection team; F represents the wind force; k represents the wind force influence coefficient; and t represents the adjusted flight time. This indicates the flight time before the adjustment.

4. The method according to claim 3, characterized in that, The method further includes: The distance between the projection of the fault detection drone on the rotation plane of the faulty wind turbine blade and the center of rotation, and the radial distance of the fault detection drone relative to the faulty wind turbine blade per unit time are obtained. The first target velocity v1 of the fault detection drone as it moves from the inside to the outside of the faulty wind turbine blade is calculated using the following formula: in, The radial distance that the fault detection drone moves relative to the faulty wind turbine blade per unit time. The rotational speed of the faulty fan is represented by d, and the distance between the projection of the fault detection drone on the rotational plane of the faulty fan blades and the center of rotation is represented by d. The fault detection drone is controlled to fly at the first target speed as it moves from the inside to the outside of the faulty wind turbine blade.

5. The method according to claim 4, characterized in that, The method further includes: The second target velocity v2 of the fault detection drone as it moves from the outside to the inside of the faulty wind turbine blade is calculated using the following formula: The fault detection drone is controlled to fly at the second target speed as it moves from the outside to the inside of the faulty wind turbine blade.

6. A fault detection device for a wind power system, characterized in that, The wind power system includes multiple wind turbines, and the device includes: The first determining module is used to determine the rotational speed of the plurality of fans; The second determining module is used to determine the average speed and the maximum speed of the plurality of fans; The third determining module is used to determine the number of fans whose rotational speed is not less than the average rotational speed; The fourth determining module is used to determine whether the plurality of fans have malfunctioned based on the average rotational speed of the plurality of fans, the maximum rotational speed of the plurality of fans, and the number of fans whose rotational speed is not less than the average rotational speed. The fourth determining module includes a calculation submodule and a first determining submodule; The calculation submodule is used to calculate the speed threshold according to the following formula. : in, This represents the average rotational speed of the multiple fans. The value of the rotational speed of the plurality of fans is represented by m, the number of the plurality of fans is represented by n, and the number of fans whose rotational speed is not less than the average rotational speed is represented by n. The rotational speed threshold represents the lowest rotational speed of the plurality of fans when no faults occur at the same wind speed. The first determining submodule is used to determine the fan with a rotation speed less than the rotation speed threshold as a faulty fan.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method described in any one of claims 1-5.

8. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-5.

9. A wind power system, characterized in that, include; Multiple fans; The electronic device according to claim 8; The drone inspection team includes: Wind detection drones are used to detect wind direction and wind speed in the environment. A unified drone was used to capture images of the multiple wind turbines; A fault detection drone is used to collect images of the blades of the faulty wind turbine; A safety control drone is used to determine the distance between the projection of the fault detection drone on the rotation plane of the faulty wind turbine blade and the center of rotation, and the radial distance the fault detection drone moves relative to the faulty wind turbine blade per unit time.

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