A Method for Disaster Status Classification of Transmission Line Towers by Integrating Particle Swarm Optimization and Extreme Learning Machine
[1]Chen Binghuang,Miao Xiren,Jiang Yan,et al.A Method for Disaster Status Classification of Transmission Line Towers by Integrating Particle Swarm Optimization and Extreme Learning Machine[J].Journal of Zhengzhou University (Engineering Science),2021,42(04):77-83.[doi:10.13705/j.issn.1671-6833.2021.04.004]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
42
Number of periods:
2021 04
Page number:
77-83
Column:
Public date:
2021-07-30
- Title:
- A Method for Disaster Status Classification of Transmission Line Towers by Integrating Particle Swarm Optimization and Extreme Learning Machine
- Keywords:
- particle swarm optimization; extreme learning machine; transmission line tower; gray correlation analysis
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2021.04.004
- Abstract:
- The damage of natural disasters to transmission lines could affect the safety of power grid operation seriously.However,it would be difficult to evaluate the classification of transmission line towers accurately during emergency inspection by drone.Based on the emergency inspection of transmission lines by drone,this paper proposed a classification method of transmission line tower that integrated particle swarm optimization and extreme learning machine.Transmission line tower disaster state could be divided into three types:normal,half collapse and full collapse.Firstly,the disaster state image data set of the towers of the transmission line emergency inspection was established,and the tower contour and its 7 main characteristic parameters were extracted from the image data set by combining the linear segment detection and Harris corner detection method.Then,the grey relation analysis method was used to obtain the 4 key characteristic parameters associated with the tower image and the disaster state.Then,the classification accuracy of towers was taken as the fitness of particle swarm optimization algorithm,and the hidden weights and hidden deviation threshold were optimized by using particle swarm optimization algorithm.The weights were imported into the extreme learning machine to train the four key characteristic parameters of the tower disaster state images.Finally,it was applied to the emergency inspection to classify the disaster state images of transmission line towers.It was found that the four key characteristic parameters of transmission line tower image and disaster status classification were circularity,length-width ratio,rectangularity and relative position of gravity center.The experimental results showed that the classification accuracy of the fusion particle swarm optimization and extreme learning machine model was 88.33%,and the accuracy rate was 92.68%.Compared with the backpropagation neural network and support vector machine algorithm model,it had better detection and classification effect.At the same time,the feasibility and effectiveness of the classification method based on particle swarm optimization and extreme learning machine were verified.
- References:
-
[1] 缪希仁,刘志颖,鄢齐晨.无人机输电线路智能巡检技术综述[J].福州大学学报(自然科学版),2020,48(2):198-209.[2] SAMPEDRO C,MARTINEZ C,CHAUHAN A,et al.A supervised approach to electric tower detection and classification for power line inspection[C]//2014 International Joint Conference on Neural Networks (IJCNN).Piscataway:IEEE,2014:1970-1977.[3] STEIGER O,LUCAS E,MARET Y.Automatic detection of transmission towers[C]//Sensors,2014 IEEE.Piscataway:IEEE,2014:1034-1037.[4] ZENG T,GAO Q,DING Z G,et al.Power transmission tower detection based on polar coordinate semivariogram in high-resolution SAR image[J].IEEE geoscience and remote sensing letters,2017,14(12):2200-2204.[5] 刘奕,吴兆鑫,徐梁刚,等.基于高分辨率SAR影像的高压输电线路杆塔检测方法[J].电力科学与技术学报,2012,27(3):47-51.
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Last Update:
2021-08-26