Fiber Bragg Grating Sensor for Fault Detection in Radial and Network Transmission Lines

Moghadas, Amin; Shadaram, Mehdi

doi:10.3390/s101009407

Cited by 10 publications

(4 citation statements)

References 24 publications

(24 reference statements)

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“…Opto-magnetic current sensors were developed in [123] by attaching fibre gratings to magnetostrictive materials such as Terfenol-D, which produces a strain due to the AC magnetic field around the current flowing in a conductor. Simulations on radial and network power systems with opto-magnetic current sensors placed 3 cm away from each phase conductor showed that fault currents can be detected and used for relay operation [124]. An alternative FBG-based current measurement device was built using a piezoelectric stack connected to a Rogowski coil [125].…”

Section: Fault Detection For Power System Protectionmentioning

confidence: 99%

Fibre Bragg Grating Sensors for Condition Monitoring of High-Voltage Assets: A Review

Ramnarine,

Peesapati,

Djurović

2023

Energies

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The high-voltage (HV) assets in the existing power transmission network will experience increased electrical, thermal, environmental and mechanical stresses and, therefore, robust condition monitoring is critical for power system reliability planning. Fibre Bragg grating (FBG) sensors offer a promising technology in HV applications due to their immunity to electromagnetic interference and multiplexing capability. This paper reviews the current technology readiness levels of FBG sensors for condition monitoring of transformers, transmission lines, towers, overhead insulators and power cables, with the aim of stimulating further development and deployment of fibre-based HV asset management systems. Currently, there are several reported cases of FBG sensors used for condition monitoring of HV assets in the field, proving their feasibility for long-term use in the power grid. The review shows that FBG technology is versatile and can facilitate multi-parameter measurements, which will standardise the demodulation equipment and reduce challenges with integrating different sensing technologies.

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Section: Fault Detection For Power System Protectionmentioning

confidence: 99%

Fibre Bragg Grating Sensors for Condition Monitoring of High-Voltage Assets: A Review

Ramnarine,

Peesapati,

Djurović

2023

Energies

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“…The FBG is an optical fiber that serves as a filter for a specific wavelength of light. The principle of the FBGS is to detect the reflected Bragg wavelength shift owing to changes in temperature, strain, or pressure [72], [73]. Fiber Bragg gratings are commonly used optical fiber sensors for measuring temperature and/or mechanical strain.…”

Section: Fiber Optic-based Sensorsmentioning

confidence: 99%

Ballistocardiogram signal processing: a review

Sadek

Biswas

Abdulrazak

2019

Health Inf Sci Syst

137

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There are several algorithms for analyzing and interpreting cardiorespiratory signals obtained from in-bed based sensors. In sum, these algorithms can be broadly grouped into three categories: time-domain algorithms, frequency-domain algorithms, and wavelet-domain algorithms. A summary of these algorithms is given below to highlight which category of algorithms will be used in our analysis. First, time-domain algorithms are mainly focused on detecting local maxima or local minima using a moving window, and therefore finding the interval between the dominant J-peaks of ballistocardiogram signal. However, this approach has many limitations because of the nonlinear and nonstationary behavior of the ballistocardiogram signal. The implication is that the ballistocardiogram signal does not display consistent J-peaks, which can usually be the case for overnight, in-home monitoring, particularly with frail elderly. Additionally, its accuracy will be undoubtedly affected by motion artifacts. Second, frequency-domain algorithms do not provide information about interbeat intervals. Nevertheless, they can provide information about heart rate variability. This is usually done by taking the fast Fourier transform or the inverse Fourier transform of the logarithm of the estimated spectrum, i.e., cepstrum of the signal using a sliding window. Thereafter, the dominant frequency is obtained in a particular frequency range. The limit of these algorithms is that the peak in the spectrum may get wider and multiple peaks may appear, which might cause a problem in measuring the vital signs. At last, the objective of wavelet-domain algorithms is to decompose the signal into different components, hence the component which shows an agreement with the vital signs can be selected. In other words, the selected component contains only information about the heart cycles or respiratory cycles, respectively. Interbeat intervals can be found easily by applying a simple peak detector. An empirical mode decomposition is an alternative approach to wavelet decomposition, and it is also a very suitable approach to cope with nonlinear and nonstationary signals such as cardiorespiratory signals. Apart from the above-mentioned algorithms, machine learning approaches have been implemented for measuring heartbeats. However, manual labeling of training data is a restricting property. Furthermore, the training step should be repeated whenever the data collection protocol has been changed.

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“…FBG sensors have been applied for use in a wide variety of novel uses in medicine, structural health monitoring, environmental monitoring, etc . [ 20 – 26 ].…”

Section: Sensor Designmentioning

confidence: 99%

FBG Sensor for Contact Level Monitoring and Prediction of Perforation in Cardiac Ablation

Razavi²,

Nazeri³

et al. 2012

Sensors

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Atrial fibrillation (AF) is the most common type of arrhythmia, and is characterized by a disordered contractile activity of the atria (top chambers of the heart). A popular treatment for AF is radiofrequency (RF) ablation. In about 2.4% of cardiac RF ablation procedures, the catheter is accidently pushed through the heart wall due to the application of excessive force. Despite the various capabilities of currently available technology, there has yet to be any data establishing how cardiac perforation can be reliably predicted. Thus, two new FBG based sensor prototypes were developed to monitor contact levels and predict perforation. Two live sheep were utilized during the study. It was observed during operation that peaks appeared in rhythm with the heart rate whenever firm contact was made between the sensor and the endocardial wall. The magnitude of these peaks varied with pressure applied by the operator. Lastly, transmural perforation of the left atrial wall was characterized by a visible loading phase and a rapid signal drop-off correlating to perforation. A possible pre-perforation signal was observed for the epoxy-based sensor in the form of a slight signal reversal (12–26% of loading phase magnitude) prior to perforation (occurring over 8 s).

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Fiber Bragg Grating Sensor for Fault Detection in Radial and Network Transmission Lines

Cited by 10 publications

References 24 publications

Fibre Bragg Grating Sensors for Condition Monitoring of High-Voltage Assets: A Review

Fibre Bragg Grating Sensors for Condition Monitoring of High-Voltage Assets: A Review

Ballistocardiogram signal processing: a review

FBG Sensor for Contact Level Monitoring and Prediction of Perforation in Cardiac Ablation

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