The maintenance and inspection of power transformers can be a time-consuming task for electric utilities, but it is a necessity for maintaining electrical grid reliability. A standard strategy for diagnosis of fault conditions in an oil-filled transformer is to periodically acquire oil samples and perform dissolved gas analysis (DGA). Aging and temperature variation can induce varying concentrations of hydrogen, methane, and other hydrocarbons, which all form as the oil degrades. Acetylene (C2H2) is generated only during localized, high-temperature events such as partial discharge, and its presence is a key marker for identifying these conditions..The development of optical fiber-based sensors to fill the role of DGA offers several advantages, including the option to implement real-time, in-situ, or even spatially resolved (distributed or quasi-distributed) sensing schemes. The evanescent field approach, in conjunction with tailored sensing materials, provides a cheap and scalable solution to this problem. However, this solution is oftentimes hampered by long-term stability and cross-sensitivity issues. One solution is to gather data from multiple optical fiber sensors designed to eliminate cross-sensitivity and calibrate drift. In this work, a multi-sensor array is developed to target multiple gas species relevant to transformer monitoring (C2H2, CH4, H2). This approach, combined with machine learning models such as support vector machines (SVM), can be used to identify the gas species present at concentrations relevant to DGA (ppm levels) with dramatically increased accuracy.
High-temperature, chemically harsh processes underpin a wide range of applications ranging from power generation, infrastructure monitoring, chemical manufacturing, and many others. For such processes, in situ sensor data is a valuable tool for both optimization and safety, however, traditional sensor platforms can be limited in terms of stability at high temperatures or under highly corrosive, reducing, or oxidizing chemical conditions. Optical fiber-based sensing offers a unique tool for this type of harsh environment sensing application. Off-the-shelf silica fiber itself is highly stable up to ~800 °C, under a wide range of chemical conditions; while single crystal optical fiber expands this operational range even further, to temperatures well above 1000 °C. Work will be presented on the utilization of n-type semiconducting oxide thin films on single crystal sapphire fiber for the evanescent field-based sensing of reducing gas streams at temperatures up to 900 °C. The role of oxygen defects on the electrical and optical properties of the relevant films will be discussed, providing a theoretical background for the observed sensing response, time-dependence, and stability. Doped SrTiO3 systems (LaxSr1-xTiO3) will be discussed for hydrogen sensing at high temperatures. Strategies and challenges associated with pushing sensor and single crystal fiber performance above 1000 °C will also be discussed.
To achieve high-efficiency turbine engine operation, turbine combustors must operate with a finely controlled fuel-air ratio near the flame extinction limit, informed by feedback from reliable in-situ temperature measurements. Distributed temperature sensing up to 900-1000 o C using Raman optical-time-domain-reflectometry (ROTDR) with single-crystal optical fiber is demonstrated in a combustion test rig. The distributed temperature sensing (DTS) system utilizes sapphire and yttrium-aluminum-garnet (YAG) fibers which were optimized to improve the signal-to-noise ratio (SNR) of collected Raman signals. Estimation of the SNR of recorded signals and predicted errors were analyzed to simulate the effect of a variety of system parameters and experimental conditions. Enhancement of the SNR through selective doping of the singlecrystal fiber was investigated. The expected Stokes and Anti-Stokes collection efficiencies using high-sensitivity avalanche photodiodes were calculated for the optimized optical path. Denoising algorithms of the SNR were developed by exploring noise sources which constrain detection capability via uncorrelated and multiplicative noise. Calibration techniques have been implemented to correct the dynamic variation of the optical loss with temperature in the singlecrystal fibers to obtain the calibration parameters and temperature profile.
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