Machine learning-based system for fault detection on anchor rods of cable-stayed power transmission towers

2021 ◽  
Vol 194 ◽  
pp. 107106
Author(s):  
M.S. Coutinho ◽  
L.R.G.S. Lourenço Novo ◽  
M.T. de Melo ◽  
L.H.A. de Medeiros ◽  
D.C.P. Barbosa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Power Transmission ◽  
Transmission Towers

scholarly journals Fault Detection in the Wind Farm Turbine Using Machine Learning Based On SVM Algorithm

2021 ◽  
Vol 1964 (5) ◽  
pp. 052015
Author(s):  
S Muthukrishnan ◽  
Arun Kumar Pallekonda ◽  
R Saravanan ◽  
B Meenakshi
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Wind Farm ◽  
Svm Algorithm

Application of Machine Learning in Industrial Boilers: Fault Detection, Diagnosis, and Prognosis

2021 ◽  
Author(s):  
Yang Meng ◽  
Xinyun Wu ◽  
Jumoke Oladejo ◽  
Xinyue Dong ◽  
Zhiqian Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Diagnosis And Prognosis ◽  
Industrial Boilers

Ultrasonic unilateral double-position excitation lamb wave defect detection and quantification method for ground electrode of transmission tower

2021 ◽  
pp. 1-15
Author(s):  
Kuan Ye ◽  
Kai Zhou ◽  
Ren Zhigang ◽  
Ruizhe Zhang ◽  
Chunsheng Li ◽  
...  
Keyword(s):  
Guided Waves ◽  
Power Transmission ◽  
Geometric Model ◽  
Guided Wave ◽  
Quantization Error ◽  
Dispersion Function ◽  
Stable Operation ◽  
Transmission Tower ◽  
Ultrasonic Guided Wave ◽  
Transmission Towers

The power transmission tower’s ground electrode defect will affect its normal current dispersion function and threaten the power system’s safe and stable operation and even personal safety. Aiming at the problem that the buried grounding grid is difficult to be detected, this paper proposes a method for identifying the ground electrode defects of transmission towers based on single-side multi-point excited ultrasonic guided waves. The geometric model, ultrasonic excitation model, and physical model are established, and the feasibility of ultrasonic guided wave detection is verified through the simulation and experiment. In actual inspection, it is equally important to determine the specific location of the defect. Therefore, a multi-point excitation method is proposed to determine the defect’s actual position by combining the ultrasonic guided wave signals at different excitation positions. Besides, the precise quantification of flat steel grounding electrode defects is achieved through the feature extraction-neural network method. Field test results show that, compared with the commercial double-sided excitation transducer, the single-sided excitation transducer proposed in this paper has a lower defect quantization error in defect quantification. The average quantization error is reduced by approximately 76%.

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