CN110879607A - Offshore wind power blade detection method based on multi-unmanned aerial vehicle formation cooperative detection - Google Patents
Offshore wind power blade detection method based on multi-unmanned aerial vehicle formation cooperative detection Download PDFInfo
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Abstract
The invention discloses an offshore wind power blade detection method based on multi-unmanned aerial vehicle formation cooperative detection, which is characterized in that a virtual structure method is adopted to form a plurality of unmanned aerial vehicles according to the three-dimensional modeling and UAV flight path planning of a system, a UAV single-machine motion model and a formation space relative motion model are built, and a distributed NMPC controller is designed; the method comprises the following steps of flying according to a specified formation, carrying out regular large-area multi-angle shooting on the offshore wind power blade, avoiding when meeting obstacles and keeping the formation; and after the shooting is finished, transmitting the obtained picture to a terminal server, carrying out image splicing processing by using a computer, carrying out image dodging by adopting a multi-chip color equalization algorithm based on a Wallis filter, obtaining a splicing effect picture, and finishing damage calibration. The detection method successfully solves the problems that the shooting range of a single unmanned aerial vehicle is limited, the carried equipment is insufficient, accidents are easy to occur and the like in the detection process, so that the human resources are saved, and the working efficiency is improved.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to an offshore wind power blade detection method based on multi-unmanned aerial vehicle formation cooperative detection.
Background
With the gradual development of high-quality wind energy resources on land, offshore wind power generation has become a future development trend and is emphasized by various countries in the world. However, in the process of development and utilization of ocean wind energy, compared with land wind energy, offshore wind energy is in a bad ocean environment, and not only corrosion problems such as high humidity and high salt fog exist, but also physical collision damage and other problems exist. The blade is a very key part in the wind generating set, and in the field of offshore wind power blade detection, modes such as manual detection, visual observation, telescope detection and the like are mainly adopted at present. In the conventional detection process, the detection is limited by the aspects of precision, safety and the like, and the problems of low efficiency, high risk, high cost and the like exist in the detection.
The existing unmanned aerial vehicle detection method is more and more extensive, and compared with manual detection, the unmanned aerial vehicle detection method has more prominent advantages. However, the single drone detection effect is not very good. On one hand, a detection platform mounted by a single unmanned aerial vehicle has limited performance, can generally only acquire target information in a limited range, and cannot meet the requirements of searching and detecting large-area targets; on the other hand, a fan tower and a turbine are also key detection parts, but a single unmanned aerial vehicle is difficult to approach the turbine of the fan, the detection mode has the limitations of high operation difficulty and the like, the fault rate and the damage rate of the unmanned aerial vehicle are also greatly increased, all detail images cannot be completely acquired, comprehensive detection is difficult to achieve, and human resources and capital investment are wasted.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for detecting offshore wind power blades based on multi-unmanned aerial vehicle formation cooperative detection.
The purpose of the invention is realized by the following technical scheme: a method for detecting offshore wind turbine blades based on multi-unmanned aerial vehicle formation cooperative detection comprises the following steps:
(1) and operating the single unmanned aerial vehicle to shoot the large-range blades in multiple integral visual angles, then transmitting the shot pictures to a terminal server for analysis and processing, and completing the three-dimensional modeling of the system and the UAV flight path planning.
(2) According to the three-dimensional modeling of the system and the UAV track planning, a plurality of unmanned aerial vehicles are formed, and the forming strategy adopted by the invention is a virtual structure method. The method considers the whole formation as a whole, simplifies the description and distribution of tasks, and comprises the following contents:
1. and (5) building a UAV single-machine motion model.
2. And (5) building a formation space relative motion model.
3. A distributed NMPC controller is designed.
(3) The UAV flight path information that will plan is sent for every unmanned aerial vehicle, then many unmanned aerial vehicles lift off, and according to the formation action flight of regulation, every unmanned aerial vehicle collocation high power zoom camera carries out the multi-angle shooting to the blade at the angle of difference, meets the obstacle and can avoid and can keep formation, guarantees the integrality that many UAVs shot by a large scale.
(4) After the cooperative photographing of the multiple unmanned aerial vehicles is completed; and transmitting the acquired photos to a terminal server, performing image splicing treatment by using a computer, and identifying dangerous factors such as cracks. The specific processing method comprises the steps of sequentially inlaying the pictures according to the image sequence, carrying out image equalization operation by adopting a multi-chip color equalization algorithm based on a Wallis filter to obtain a splicing effect picture, and finishing damage calibration of the blades, so that the defect part is more easily detected.
Compared with the prior art, the invention has the following beneficial effects:
(1) the invention adopts the multi-unmanned aerial vehicle formation cooperative detection, can more accurately and conveniently detect the defect of the blade, and successfully solves the defects of high cost, high danger, low efficiency and the like of the traditional manual detection.
(2) The unmanned aerial vehicle formation is widely applied in military, and the technology is applied to offshore wind power blade detection, so that great breakthrough is achieved. Many unmanned aerial vehicles shoot in coordination that the sharing of accessible resource and information realizes the parallel execution of task, enlarges detection range, shortens time, improves work efficiency, has successfully solved single unmanned aerial vehicle in the testing process moreover, and the scope of shooing is limited, and the equipment of carrying is not enough, and special parts can't shoot and the easy accident scheduling problem that appears.
(3) The unmanned aerial vehicle formation mode adopts a virtual structure method, and the motion track of the unmanned aerial vehicle formation method is a formation reference track. The method has the advantages that the whole formation is regarded as a whole, the description and distribution of tasks are simplified, the behavior of the cluster can be easily specified, and the implementation of a distributed control strategy is easy.
(4) According to the invention, by adopting the image splicing processing method, the images shot by the unmanned aerial vehicle are spliced and homogenized, so that the obtained images are truer and clearer, the problems of image blurring, dark color and the like caused by factors such as shooting jitter and exposure of the unmanned aerial vehicle are avoided, and the defects of the blades are more clearly seen.
Drawings
Fig. 1 is a diagram of relative motion analysis of UAV formation.
Fig. 2 is a block diagram of NMPC solution.
Fig. 3 is a diagram of UAV formation detection trajectories.
Fig. 4 is an image stitching flowchart.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making an invasive task, are within the scope of the present invention.
As shown in fig. 1, the present invention builds a system model, which includes:
1. UAV standalone movement model.
Assuming that the UAV flight angle of attack and sideslip are small and wind field effects are not considered, the available single-machine motion models for UAVs are as follows:
wherein: vi、χiAnd gammaiAre respectively UAViSpeed, heading angle, and climb angle; (x)i,yi,zi) Is a UAViA coordinate at an inertial system position; control input uxi、uyiAnd uziThrow of the overload along the x-axis, y-axis and z-axis of the track coordinate system, respectivelyShadow, and are respectively limited to [ u ]ximin,uximax]、[uyimin,uyimax]、[uzimin,uzimax]Internal; the velocity constraint of each UAV is 0<Vimin≤ Vi≤Vimax.
2. And forming a spatial relative motion model.
The invention adopts a virtual structure method to define formation configuration, and assumes that a moving virtual point O existsfFlying along a given formation reference track, and defining a formation coordinate system O by taking the virtual point as an originfxfyfzfAnd x isfDirection of projection component of axis along virtual point velocity in horizontal plane, zfAxis vertical to horizontal and facing downwards, yfThe axes lie in the horizontal plane as determined by the right hand rule, as shown in fig. 1.
Compared with the traditional virtual structure method which adopts a virtual point track system as a formation reference system, the formation coordinate system defined herein can more intuitively define the formation configuration, and the formation relative motion equation based on the coordinate system has a simpler form and is easy to control and realize. In fig. 1: o isgxgygzgIs an inertial system, Vf、χfAnd gammafRespectively, the virtual point speed, the course angle and the climbing angle, (x)if,yif,zif) Is a UAViRelative position coordinates under the formation system, the desired formation configuration can be represented by a set of relative position coordinates under the formation system { (x)dif,ydif,zdif) I is defined as 1,2, …, m, and m is the total number of formation UAVs.
It is assumed that the virtual point reference trajectory (i.e., the formation reference trajectory) is given by the following differential equation based on the time parameter:
wherein: (x)f,yf,zf) The position of a virtual point under an inertial system; omegafIs a virtual point course angular velocity, and Vf、ωfAnd gammafPiecewise continuous functions all of which are timeAnd the data are given in advance by a formation path planning system.
In the figure Ri、RfAnd RifThe position vector of the UAV under the inertial system, the virtual point position vector and the relative position vector between them, they satisfy the following trigonometric relationship:
Rif＝Ri-Rf. (3)
and (3) obtaining the following formation space relative motion equation by deriving the above formula with respect to time and projecting the result to a formation coordinate system according to the conversion relation between the coordinate systems:
wherein: heading angle error chiif＝χi-χfFormation hold error χeif＝χif-χdif，yeif＝yif-ydif，zeif＝ zif-zdif.
In summary, combining the UAV standalone motion model (1) and the formation space relative motion model (4), the UAV formation flight system model can be obtained as follows:
where the virtual point motion state can be considered as a known disturbance. If x is orderedi＝[Vi,χi,γi,xeif,yeif,zeif]T,ui＝[uxi,uyi,uzi]TThen equation (5) can be abbreviated as the following discrete state space form:
xi(k+1)＝xi(k)+f(xi(k),ui(k))Δt,
i＝1,2,…,m. (6)
wherein: and delta t is a discrete time step, and f (×) is a nonlinear function corresponding to the right side of all sub-formula equal signs in the formula (5).
As shown in fig. 2, the invention designs a distributed NMPC controller, and solves the unmanned aerial vehicle control input at each sampling moment by using NMPC method online rolling optimization. FIG. 2 is a solving framework for the NMPC formation controller, where k is the current time, N is the prediction time domain and control time domain length, { xi(k+1|k),…xi(k + N) k is the UAV state vector predictor, { xir(k+1|k),…xir(k + N) k is the UAV reference state vector, { ui(k|k),…uiAnd (k + N-1) k is an optimal control input sequence. According to the structure of the controller, each UAV independently solves the optimization problem, and only communicates with other UAVs when necessary, so that the controller is a distributed controller.
In order to realize formation maintenance, obstacle avoidance and collision avoidance control targets in a dynamic environment, an objective function in the following form can be established:
wherein: item 1 and item 2 are input cost and state cost respectively, and the maintenance of the formation form is realized; item 3 is a terminal state penalty cost for ensuring the stability of the NMPC controller; item 4 and item 5 are respectively an UAV obstacle avoidance and a collision avoidance penalty; r, S, SN are corresponding weight matrices.
As shown in fig. 3, the unmanned aerial vehicle flight path planning method is used for unmanned aerial vehicle flight path planning aiming at the structure and the position of the offshore wind power blade. Assuming that a large-area blade area to be detected is a rectangular area, the length of the area is L, and the width of the area is W; according to the formation management of multiple unmanned aerial vehicles, the distance of each unmanned aerial vehicle in the horizontal direction is greater than a, so that the unmanned aerial vehicles can be prevented from colliding with each other under emergency. In the process of detecting the offshore wind power blade, the speed of the unmanned aerial vehicle is kept at a constant value VmAnd the radius of the shooting area of a single unmanned aerial vehicle is a, so that the unmanned aerial vehicle as few as possible is used for shooting the designated area in real objects and acquiring photos. The following steps are specific steps of navigation:
(1) and 5 or more unmanned aerial vehicles are selected to start to take off from the right side A of the lower boundary on the sea, and the flying direction of the unmanned aerial vehicles is vertical to the upper side and flies at a constant speed.
(2) The multiple unmanned planes start to shoot the blades and store the pictures; when the unmanned aerial vehicle reaches the upper boundary BC of the limited area, the unmanned aerial vehicle formation flies in left turn, straight line and left turn, reaches a new path, flies along the new path and takes pictures.
(3) When the unmanned aerial vehicle reaches the lower boundary AD of the limited area, all shooting of the area is finished, and at the moment, the unmanned aerial vehicle can be controlled to leave the area, navigate to another specified area and continue shooting; and the camera can fly and shoot again along the previous path, so that more comprehensive and more accurate shooting can be realized, and more pictures can be obtained for processing.
(4) When meeting the obstacle, unmanned aerial vehicle intelligence avoids the obstacle, and formation is dismissed earlier, and the back is reorganized and is kept the original state and continues to fly and shoot.
As shown in fig. 4, the invention adopts a processing method of image splicing to splice images shot by the unmanned aerial vehicle through the computer to obtain clear and complete images. The method mainly comprises the following steps:
(1) firstly, a target area integral splicing line network is constructed by adopting a Voronoi diagram based on a plane considering overlapping.
(2) The overlapping range of each pair of adjacent images is determined, and image splicing lines are generated, so that redundant and useless data can be eliminated, and the calculation amount of a computer program is reduced.
(3) And extracting the central axis of the overlapped area according to the central axis algorithm of the simple polygon and the convex polygon.
(4) Generating Voronoi polygons considering the overlapping, sequentially dividing and clipping the corresponding unmanned aerial vehicle images by using the bisection central axis, removing edge parts with larger deformation, and generating the Voronoi polygons to which the images belong so as to generate Voronoi diagrams of all the images.
(5) And calculating and storing the public side information, namely generating a splicing line, and connecting the vertexes of the splicing lines according to a certain rule, so that a splicing line network of the whole aerial photographing area can be constructed, and the good splicing of the unmanned aerial vehicle images is realized.
(6) Aiming at the phenomenon of unbalanced colors of images of the unmanned aerial vehicle, a Wallis filter is adopted to carry out image dodging operation, the Wallis filter is utilized to adjust the linear distribution of the image gray levels by counting the mean value and the variance of the image gray levels, the consistency of the color tones and the brightness among a plurality of images is kept, and the dodging processing of the aerial images of the unmanned aerial vehicle can be better realized.
(7) And obtaining a complete mosaic image, detecting and identifying the defect characteristics of the blade, and completing the damage calibration of the blade.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A method for detecting an offshore wind power blade based on multi-unmanned aerial vehicle formation cooperative detection is characterized by comprising the following steps: the method comprises the following steps:
(1) and operating the single unmanned aerial vehicle to shoot the large-range blades in multiple integral visual angles, then transmitting the shot pictures to a terminal server for analysis and processing, and completing the three-dimensional modeling of the system and the UAV flight path planning.
(2) According to the three-dimensional modeling and UAV track planning of the system, a plurality of unmanned aerial vehicles are formed, and the forming strategy adopted by the invention is a virtual structure method. The method considers the whole formation as a whole, simplifies the description and distribution of tasks, and comprises the following contents:
1. and (5) building a UAV single-machine motion model.
2. And (5) building a formation space relative motion model.
3. A distributed NMPC controller is designed.
(3) The UAV flight path information that will plan is sent for every unmanned aerial vehicle, then many unmanned aerial vehicles lift off, and according to the formation action flight of regulation, every unmanned aerial vehicle collocation high power zoom camera carries out the multi-angle shooting to the blade at the angle of difference, meets the obstacle and can avoid and can keep formation, guarantees the integrality that many UAVs shot by a large scale.
(4) After the cooperative photographing of the multiple unmanned aerial vehicles is completed; and transmitting the acquired photos to a terminal server, performing image splicing treatment by using a computer, and identifying dangerous factors such as cracks. The specific processing method comprises the steps of sequentially inlaying the pictures according to the image sequence, carrying out image equalization operation by adopting a multi-chip color equalization algorithm based on a Wallis filter to obtain a splicing effect picture, and finishing the damage calibration of the blades, so that the defect part is more easily detected.
2. The offshore wind power blade detection method based on multi-unmanned aerial vehicle formation cooperative detection as claimed in claim 1, wherein: and (2) in the step (1), the single unmanned aerial vehicle is controlled to carry out integral shooting on the offshore wind power blade, and the picture is processed to complete system modeling, so that flight path planning of formation of multiple unmanned aerial vehicles is facilitated.
3. The method of claim 2, wherein: the aerial photographing parameters comprise flight lines, heights, speeds, photographing distances, positions, time and the like.
4. The offshore wind power blade detection method based on multi-unmanned aerial vehicle formation cooperative detection as claimed in claim 1, wherein: and (3) adopting a virtual structure method to form a plurality of unmanned aerial vehicles in the step (2), building a formation model, and deducing a motion equation according to a three-dimensional space coordinate system.
5. The method of claim 4, wherein: and establishing a target function, and realizing formation maintenance, obstacle avoidance and collision avoidance control targets in a dynamic environment.
6. The method of claim 5, wherein: and when the NMPC is solved, the UAV state prediction is needed, a cost function is obtained according to the reference track and the expected formation, and finally, optimization calculation is carried out.
7. The offshore wind power blade detection method based on multi-unmanned aerial vehicle formation cooperative detection as claimed in claim 1, wherein: in the step (3), regular large-area multi-angle shooting can be performed in the blade area by adopting a target vertical line searching method on tracks detected by a plurality of unmanned aerial vehicles in formation.
8. The offshore wind power blade detection method based on multi-unmanned aerial vehicle formation cooperative detection as claimed in claim 1, wherein: the image splicing processing is carried out on the pictures collected by the unmanned aerial vehicle in the step (4), and the steps comprise:
1. consider overlapping surface Voronoi diagrams.
2. And determining the image overlapping range.
3. And extracting a central axis of the overlapping area.
4. A surface Voronoi polygon is generated that accounts for the overlap.
5. And (4) image dodging based on a Wallis filter.
6. And obtaining a complete and clear splicing diagram, detecting and identifying the defect characteristics of the blade, and completing the damage calibration of the blade.
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| CN113743208A (en) * | 2021-07-30 | 2021-12-03 | 南方海洋科学与工程广东省实验室(广州) | Unmanned aerial vehicle array-based white dolphin number statistical method and system |
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Application publication date: 20200313 |