Real-Time Monitoring of Temperature Rises of Energized Transformer Cores With Distributed Optical Fiber Sensors

Lü, Ping; Buric, Michael; Byerly, Kevin; Moon, Seung Ryul; Nazmunnahar, M.; Simizu, S.; Leary, Alex; Beddingfield, Richard; Sun, Chaoyang; Zandhuis, Paul H.; McHenry, Michael E.; Ohodnicki, Paul R.

doi:10.1109/tpwrd.2019.2912866

Cited by 51 publications

(14 citation statements)

References 36 publications

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“…[17] Recently, Lu et al demonstrated real-time temperature monitoring of energized transformer cores using an OFDR system. [18] The temperature change in different parts of a transformer was measured for 30 min with a temperature resolution of 0.1 o C. In 2020, T.S. Hudson monitored basal icequakes/earthquakes at Rutford Ice Stream, Antarctica, using a DAS system (Silixa iDAS interrogator) with Figure 2.…”

Section: Latest Randd Efforts In Fiber Optic Sensing Technologiesmentioning

confidence: 99%

Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Venketeswaran

Lalam

Wuenschell

et al. 2021

Advanced Intelligent Systems

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134

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The latest digital revolution involves the rise of smart devices composed of sensor hardware and artificial intelligence (AI) software for performing intelligent tasks. Smart sensors have become ubiquitous in our lives with varied applications ranging from voice-enabled home devices (Google Home, Alexa, etc.) to the Industrial Internet of Things (IIoT). This revolution has been fueled by 1) miniaturization of sensing hardware, 2) easy access to cloud and high-performance computing, 3) development of big data storage and analytics technologies, and 4) the latest breakthroughs in machine learning (ML) and AI technologies. The emergence of AI since 2012 and its major breakthroughs can be attributed to the research and development (R&D) in deep learning, a subfield of ML that uses biologically inspired neural networks to perform learning tasks. [1] The performance of conventional ML algorithms depends on the individual selection of specific features, while deep neural networks (DNN) automatically generate features as part of the learning process. Deep learningbased AI technologies are increasingly showing performance

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Section: Latest Randd Efforts In Fiber Optic Sensing Technologiesmentioning

confidence: 99%

Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Venketeswaran

Lalam

Wuenschell

et al. 2021

Advanced Intelligent Systems

Self Cite

134

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“…The stable result in this work eliminates the potential error in point sensing with pre-defined locations and highlights the possibility of locating abnormal cracks and temperature hotspots due to cell aging. Rayleigh-based OFDR systems have also been applied to the temperature monitoring of the magnetic core and insulation oil of a power transformer by Lu et al [ 109 ] and Badar et al [ 110 , 111 ]. The above cases have confirmed the unique advantage of immunity to EMI of fiber optic sensors in high power electrical components and demonstrated their distributed sensing capability, which is highly critical to monitor the health of these electrical assets.…”

Section: Operating Principle and Recent Advances In Fiber Optic Sementioning

confidence: 99%

Fiber Optic Sensing Technologies for Battery Management Systems and Energy Storage Applications

Preger

Burroughs

et al. 2021

Sensors

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Applications of fiber optic sensors to battery monitoring have been increasing due to the growing need of enhanced battery management systems with accurate state estimations. The goal of this review is to discuss the advancements enabling the practical implementation of battery internal parameter measurements including local temperature, strain, pressure, and refractive index for general operation, as well as the external measurements such as temperature gradients and vent gas sensing for thermal runaway imminent detection. A reasonable matching is discussed between fiber optic sensors of different range capabilities with battery systems of three levels of scales, namely electric vehicle and heavy-duty electric truck battery packs, and grid-scale battery systems. The advantages of fiber optic sensors over electrical sensors are discussed, while electrochemical stability issues of fiber-implanted batteries are critically assessed. This review also includes the estimated sensing system costs for typical fiber optic sensors and identifies the high interrogation cost as one of the limitations in their practical deployment into batteries. Finally, future perspectives are considered in the implementation of fiber optics into high-value battery applications such as grid-scale energy storage fault detection and prediction systems.

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“…In recent years, distributed fiber optic sensors based on Rayleigh, Brillouin, or Raman back scattering 17 have attracted research interest and are successfully used for distributed sensing of infrastructures such as strain, 18 temperature, 19 vibration, 20 pipeline leakage, 21 corrosion, 22 and cracking. [23][24] Imai and Feng 25 employed Brillouin optical correlation domain analysis (BOCDA) based distributed optical fiber to monitor cracking of fiberreinforced concrete bridge decks.…”

Section: Introductionmentioning

confidence: 99%

Characterization of OFDR distributed optical fiber for crack monitoring considering fiber-coating interfacial slip

Zhao

Tang

et al. 2022

Structural Health Monitoring

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An analytical crack-strain transfer model considering interfacial bond-slip between optical fiber and fiber coating is proposed and experimentally validated for crack monitoring of structures. Interfacial shear stress-slip relationship between the optical fiber and fiber coating was determined by using pull-out tests, and a simplified trilinear three-stage interfacial shear stress-slip model was proposed. The model is incorporated into the development of a crack-strain transfer model between the structural substrate and the optical fiber, and the fiber strain distribution is analytically obtained and compared with the strain distribution as perfect bond is assumed between the fiber and fiber coating. Experiments were also conducted to validate the crack-strain transfer model by continuously monitoring corrosion-induced concrete cracking with OFDR distributed optical fiber. Compared with strain transfer models proposed by other researchers in the literature and perfect bond model, the crack-strain transfer model considering interfacial shear stress-slip mechanism between fiber and fiber coating agrees well with experimental results, especially for large crack monitoring.

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Real-Time Monitoring of Temperature Rises of Energized Transformer Cores With Distributed Optical Fiber Sensors

Cited by 51 publications

References 36 publications

Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Fiber Optic Sensing Technologies for Battery Management Systems and Energy Storage Applications

Characterization of OFDR distributed optical fiber for crack monitoring considering fiber-coating interfacial slip

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