Time-Division-Multiplexed Energy Harvesting From Quasi-Distributed Fiber Bragg Grating Arrays (FBGAs) Sensing Networks
This work presents a new approach to energy harvesting (EH) from a quasi-distributed sensing network of fiber Bragg grating arrays (FBGAs). While maintaining accurate FBGA temperature sensing, our approach collects the typically unused transmitted power from the broadband light across an FBGA sensin...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10937756/ |
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| Summary: | This work presents a new approach to energy harvesting (EH) from a quasi-distributed sensing network of fiber Bragg grating arrays (FBGAs). While maintaining accurate FBGA temperature sensing, our approach collects the typically unused transmitted power from the broadband light across an FBGA sensing network and converts and stores it as electrical energy to power up electronic-based sensors (EBSs). To demonstrate this concept, we reported on a quasi-distributed FBGA network topology consisting of two different FBGAs: one with 5 FBGs and the other with 10 FBGs. The system employs time-division multiplexing (TDM) via an optical switch to alternate the light between both FBGAs. Both FBGAs were calibrated for temperature sensing using their reflected spectra, showing typical sensitivity values of 11.72 pm/°C-12.43 pm/°C for FBGA1 and 12.86 pm/°C-14 pm/°C for FBGA2. The untapped power transmitted through both FBGAs was harvested using EH units based on supercapacitors. The EH process was investigated for different switching times (1 s, 100 s, 600 s, and 1000 s). The cumulative harvested power ranged from ~6.56-7.06 mW, corresponding to the overall conversion efficiency of ~25.8-27.8% for the entire system after leaving it for 60 min of temperature sensing. These results validate the potential of using quasi-distributed FBGA networks for simultaneous sensing and EH, providing a sustainable solution for autonomous multi-parameter hybrid sensing applications such as remote underwater or underground EBS. |
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| ISSN: | 2169-3536 |