A Novel Electromagnetic Energy Harvester Based on Double-Ring Core for Power Line Energy Harvesting

2020 ◽  
Vol 29 (16) ◽  
pp. 2050265
Author(s):  
Jiajia Zhang ◽  
Xingliang Tian ◽  
Jie Li ◽  
Dong Yan
Keyword(s):  
Energy Harvesting ◽  
Magnetic Flux ◽  
Power Management ◽  
Energy Harvester ◽  
Power Line ◽  
Magnetic Flux Leakage ◽  
Line Energy ◽  
Double Ring ◽  
Flux Leakage ◽  
Ring Core

A novel electromagnetic energy harvester (EMEH) based on double-ring core for power line energy harvesting is proposed in this paper. Due to large magnetic reluctance caused by the inherent air gap at the opening of core, the magnetic flux leakage in magnetic core severely limits the output power of EMEH. A double-ring core with lower magnetic flux leakage is developed. The internal magnetic reluctance of the double-ring core is reduced by changing the distribution of the air gap with a fixed volume. The simulation results show that the double-ring core can produce the highest average magnetic induction, which is 2.42 times, 1.82 times and 1.7 times that of the single-ring opening, stepped opening and V-shaped opening, respectively. In order to improve the output performance of the EMEH, the resonance matching is used for the power management. The power management unit through the resonant matching can increase the acquisition efficiency by 2.23 times and achieve a maximum output power of 32.78[Formula: see text]mW at a 10[Formula: see text]A current across the power line. The EMEH can drive a low-power wireless node operating for 29[Formula: see text]min. A valuable solution is provided for high-efficiency self-powered systems in smart applications.

scholarly journals Location Detection Method of Detector in Pipeline Using VMD Algorithm and Machine Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1436
Author(s):  
Tuoru Li ◽  
Senxiang Lu ◽  
Enjie Xu
Keyword(s):  
Machine Learning ◽  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Intrinsic Mode Functions ◽  
Detection Range ◽  
Vibration Signals ◽  
Feature Vectors ◽  
Learning Classifier ◽  
Mode Decomposition ◽  
Flux Leakage

The internal detector in a pipeline needs to use the ground marker to record the elapsed time for accurate positioning. Most existing ground markers use the magnetic flux leakage testing principle to detect whether the internal detector passes. However, this paper uses the method of detecting vibration signals to track and locate the internal detector. The Variational Mode Decomposition (VMD) algorithm is used to extract features, which solves the defect of large noise and many disturbances of vibration signals. In this way, the detection range is expanded, and some non-magnetic flux leakage internal detectors can also be located. Firstly, the extracted vibration signals are denoised by the VMD algorithm, then kurtosis value and power value are extracted from the intrinsic mode functions (IMFs) to form feature vectors, and finally the feature vectors are input into random forest and Multilayer Perceptron (MLP) for classification. Experimental research shows that the method designed in this paper, which combines VMD with a machine learning classifier, can effectively use vibration signals to locate the internal detector and has the characteristics of high accuracy and good adaptability.

Alternative magnetic flux leakage modalities for pipeline inspection

1996 ◽  
Vol 32 (3) ◽  
pp. 1581-1584 ◽  
Author(s):  
G. Katragadda ◽  
W. Lord ◽  
Y.S. Sun ◽  
S. Udpa ◽  
L. Udpa
Keyword(s):  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Pipeline Inspection ◽  
Flux Leakage

GMR-based magnetic flux leakage technique for condition monitoring of steel track rope

2011 ◽  
Vol 53 (7) ◽  
pp. 377-381 ◽  
Author(s):  
W Sharatchandra Singh ◽  
B P C Rao ◽  
C K Mukhopadhyay ◽  
T Jayakumar
Keyword(s):  
Magnetic Flux ◽  
Condition Monitoring ◽  
Magnetic Flux Leakage ◽  
Flux Leakage
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