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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.