KEYWORDS: Signal to noise ratio, Data acquisition, Tolerancing, Temperature metrology, Computing systems, Tunable filters, Sensing systems, Laser frequency, Data analysis, Statistical analysis
A Brillouin Optical Time-Domain Analysis (BOTDA) Lorentzian data fitting method to estimate the Brillouin Frequency Shift (BFS) is proposed. Data is obtained from an experimental setup used to conduct the temperature and strain measurements. Before Lorentzian fitting the noisy data is averaged and filtered. The proposed method attempts to lower computational complexity in determining the Brillouin frequency. The resulting parameters of a completed BGS curve fitting are used as initial set of parameters for the next location point BGS fitting. Completion of the Lorentzian fitting using the Levenberg-Marquardt nonlinear curve fitting algorithm is achieved in a small number of iterations which improves the performance in obtaining the Brillouin frequency shift.
We examine the use of state-of-the-art distributed sensing systems to extract temperature information from the optical fibre infrastructure already of the Electricity Authority of Cyprus power distribution network (~25-year old installation); as a means of optical fibre distributed sensing in the underground cables. The optical fibres are collocated with existing power distribution cables, for the purpose of power line monitoring cable joints that are prone to failure, along with general monitoring for unusual behaviour and potential cable fault conditions. Detection is achieved using DTS: Distributed Temperature Sensors (Silixa Ltd) that use RAMAN-based measurements in combination with BOTDR (Brillouin Optical Time-domain Reflectometry) for high-precision temperature detection. We examine the correlation between the temperature of the power cable with the power consumption provided by the EAC and the weather conditions. Furthermore, our data will give an indication of how important is uniform spacing between power and optical cables. The real-time and continuous monitoring of the temperature of the optical cables through the distributed sensing systems may help identifying abnormal cable behavior (hot spots) and possible future network failures in the power network.
We present a study on the application of machine learning to optical fibre distributed sensing, with data recovered using a state-of-the-art, commercial BOTDR distributed sensing system; temperature information was extracted from the power line distribution networks that are part of the Electricity Authority of Cyprus. A machine learning approach was implemented for the prediction task of finding points of abnormal behaviour, mimicking the power cable joints that are prone to failure, along with general monitoring for unusual behaviour and potential cable fault conditions; the task is a binary classification one. Labels “0/1” were assigned to the BOTDR measurements, with “1” corresponding to data points in space and time for which the signal showcased a problematic scenario, such as that recorded by optical fibres that are collocated with power cables where the fibre’s temperature measurement increases to dangerously high values, and conversely “0” for all other scenarios. The algorithm’s base is a variation of the state-of-the-art transformer architecture, which depends solely on attention mechanisms. The field data recovered show the potential of the algorithm to predict spatiotemporally problematic points, using the temperature measurements of the collocated fibre.
In this work we utilize multimode optical fibers for the detection of simulated errors or failures in underground power cables. It is known that in cases of failure the underground transmission cables overheat locally, they become a hot-spot, and it is extremely difficult to detect and locate the problem. The proposed methodology is as follows, having an underground electric cable we simulate various temperature profiles whilst the optical fiber was placed in selected distances away from our simulated fault to examine the detection performance of our fiber. In this way we aim to stabilize the operation of the underground cable damage detection system that is placed by the Electricity Authority of Cyprus. The EAC has certain locations where the existing single-mode optical fibres are collocated with the underground power cables, although relative spacing may not be constant. Our data will give an indication of how important is uniform spacing between power and optical cables. We examine if any change in the temperature of the power cable is also reflected in the optical fibre cable. The real-time and continuous monitoring of the temperature of the optical cables through the distributed sensing systems may help identifying abnormal cable behaviour (hot spots) and possible future network failures in the power network.
We present a study on the application of machine learning to optical fibre distributed sensing, with data recovered using a state-of-the-art, commercial BOTDR distributed sensing system, to extract temperature information from single-mode optical fibre over a 40-km distance. The application is for power line monitoring of underground cables that are collocated with optical fibres that form part of the Electricity Authority of Cyprus’ island wide power distribution networks. The existing optical fibre infrastructure acts as the sensing element, monitoring temperature changes when in close proximity to the power lines. The initial training measurements for the machine learning algorithm were recorded in a laboratory setting using temperature and humidity-controlled elements, with sections of fibre spliced to underground fibre cables subjected to temperature excursions. A machine learning approach was implemented for the prediction task of finding points that are likely to get damaged, mimicking the behavior of power cable joints that are prone to failure, along with general monitoring for unusual behavior and potential cable fault conditions; the task is a binary classification one. Labels “0/1” were assigned to the BOTDR measurements, with “1” corresponding to data points in space and time for which the signal showcased a problematic scenario, such as the collocated fibre’s temperature rising to dangerously high values, and “0” to the rest. The algorithm’s base is a variation of the state-of-the-art transformer architecture, which depends solely on attention mechanisms. The training was undertaken on the laboratory data and re-training is done periodically with new field measurements. The completion of the training phase shows the potential of the algorithm to predict spatiotemporally problematic points, using the temperature measurements of the collocated fibre; this will be extended to BOTDR data taken in the field.
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