Several Factors Affecting the Inspection of Wind Turbines by UAV

The country strongly supports the development of new energy industries,with the clean energy wind power industry developing rapidly and the market maturing,the scale of wind farms and installed capaci-ty expanding,and the blade length increasing to 60-70m.The increased blade length and weight increase the probability of damage.the manual inspection method is time-consuming and laborious,with a high economic cost,low inspection efficiency,and high safety risks,and cannot meet the current wind turbine fast and efficient inspection requirements.This paper introduces the characteristics of the type of UAV,its working status,and mode,and proposes how to determine the best area for UAV inspection according to the factors that can cause interference to the inspection in the actual wind field,to achieve the demand for fast and efficient inspection of the blade surface and improve the accuracy of inspection.It is believed that with the development of UAV technology,UAVs will play a more important role in the field of inspection.

Variable universe fuzzy control of walking stability for flying‐walking power line inspection robot based on multi‐work conditions

To address complex work conditions incredibly challenging to the stability of power line inspection robots,we design a walking mechanism and propose a variable universe fuzzy control(VUFC)method based on multi‐work conditions for flying‐walking power line inspection robots(FPLIRs).The contributions of this paper are as follows:(1)A flexible pressing component is designed to improve the adaptability of the FPLIR to the ground line slope.(2)The influence of multi‐work conditions on the FPLIR's walking stability is quantified using three condition parameters(i.e.,slope,slipping degree and swing angle),and their measurement methods are proposed.(3)The VUFC method based on the condition parameters is proposed to improve the walking stability of the FPLIR.Finally,the effect of the VUFC method on walking stability of the FPLIR is teste.The experimental results show that the maximum climbing angle of the FPLIR reaches 29.1°.Compared with the constant pressing force of 30 N,the average value of slipping degree is 0.93°,increasing by 35%.The maximum and average values of robot's swing angle are reduced by 46%and 54%,respectively.By comparing with fuzzy control,the VUFC can provide a more reasonable pressing force while maintaining the walking stability of the FPLIR.The proposed walking mechanism and the VUFC method significantly improve the stability of the FPLIR,providing a reference for structural designs and stability controls of inspection robots.

Research on a multi‑dimensional image information fusion algorithm based on NSCT transform

Traditional inspection cameras determine targets and detect defects by capturing images of their light intensity,but in complex environments,the accuracy of inspection may decrease.Information based on polarization of light can characterize various features of a material,such as the roughness,texture,and refractive index,thus improving classification and recognition of targets.This paper uses a method based on noise template threshold matching to denoise and preprocess polarized images.It also reports on design of an image fusion algorithm,based on NSCT transform,to fuse light intensity images and polarized images.The results show that the fused image improves both subjective and objective evaluation indicators,relative to the source image,and can better preserve edge information and help to improve the accuracy of target recognition.This study provides a reference for the comprehensive application of multi-dimensional optical information in power inspection.

A New Fusion Chemical Reaction Optimization Algorithm Based on Random Molecules for Multi-Rotor UAV Path Planning in Transmission Line Inspection

A fusion chemical reaction optimization algorithm based on random molecules(RMCRO) is proposed to meet the special demand of power transmission line inspection. This new algorithm improves the shortcomings of chemical reaction algorithm by merging the idea of repellent-attractant rule and accelerates convergence by using difference algorithm. The molecules in this algorithm avoid obstacles and search optimal path of transmission line inspection by using sensors on multi-rotor unmanned aerial vehicle(UAV). The option of optimal path is based on potential energy of molecules and cost function without repeated parameter adjustment and complicated computation. By compared with an improved particle swarm optimization(IMPSO) in different circumstances of simulation, it can be concluded that the new algorithm presented not only can obtain more optimal path and avoid to trap in local minimum, but also can keep related sensors in a more stable status.

Online Monitoring Method for Insulator Self-explosion Based on Edge Computing and Deep Learning

Aiming at the problems of traditional centralized cloud computing which occupies large computing resources and creates high latency,this paper proposes a fault detection scheme for insulator self-explosion based on edge computing and DL(deep learning).In order to solve the high amount of computation brought by the deep neural network and meet the limited computing resources at the edge,a lightweight SSD(Single Shot MultiBox Detector)target recognition network is designed at the edge,which adopts the MobileNets network to replace VGG16 network in the original model to reduce redundant computing.In the cloud,three detection algorithms(Faster-RCNN,Retinanet,YOLOv3)with obvious differences in detection performance are selected to obtain the coordinates and confidence of the insulator self-explosion area,and then the self-explosion fault detection of the overhead transmission line is realized by a novel multimodel fusion algorithm.The experimental results show that the proposed scheme can effectively reduce the amount of uploaded data,and the average recognition accuracy of the cloud is 95.75%.In addition,it only increases the power consumption of edge devices by about 25.6W/h in their working state.Compared with the existing online monitoring technology of insulator selfexplosion at home and abroad,the proposed scheme has the advantages of low transmission delay,low communication cost and high diagnostic accuracy,which provides a new idea for online monitoring research of power internet of things equipment.

Low‑light enhancement method with dual branch feature fusion and learnable regularized attention

Restricted by the lighting conditions,the images captured at night tend to sufer from color aberration,noise,and other unfavorable factors,making it difcult for subsequent vision-based applications.To solve this problem,we propose a two-stage size-controllable low-light enhancement method,named Dual Fusion Enhancement Net(DFEN).The whole algorithm is built on a double U-Net structure,implementing brightness adjustment and detail revision respectively.A dual branch feature fusion module is adopted to enhance its ability of feature extraction and aggregation.We also design a learnable regularized attention module to balance the enhancement efect on diferent regions.Besides,we introduce a cosine training strategy to smooth the transition of the training target from the brightness adjustment stage to the detail revision stage during the training process.The proposed DFEN is tested on several low-light datasets,and the experimental results demonstrate that the algorithm achieves superior enhancement results with the similar parameters.It is worth noting that the lightest DFEN model reaches 11 FPS for image size of 1224×10^(24)in an RTX 3090 GPU.