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紫外可见光谱法:评估变压器绝缘油颜色指数的新方法。

UV-Vis Spectroscopy: A New Approach for Assessing the Color Index of Transformer Insulating Oil.

机构信息

Institute of Power Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia.

Tenaga Nasional Berhad (TNB) Research Sdn. Bhd., Bandar Baru Bangi, Kajang 43000, Selangor, Malaysia.

出版信息

Sensors (Basel). 2018 Jul 6;18(7):2175. doi: 10.3390/s18072175.

Abstract

Monitoring the condition of transformer oil is considered to be one of the preventive maintenance measures and it is very critical in ensuring the safety as well as optimal performance of the equipment. Various oil properties and contents in oil can be monitored such as acidity, furanic compounds and color. The current method is used to determine the color index () of transformer oil produces an error of 0.5 in measurement, has high risk of human handling error, additional expense such as sampling and transportations, and limited samples can be measured per day due to safety and health reasons. Therefore, this work proposes the determination of of transformer oil using ultraviolet-to-visible (UV-Vis) spectroscopy. Results show a good correlation between the of transformer oil and the absorbance spectral responses of oils from 300 nm to 700 nm. Modeled equations were developed to relate the of the oil with the cutoff wavelength and absorbance, and with the area under the curve from 360 nm to 600 nm. These equations were verified with another set of oil samples. The equation that describes the relationship between cutoff wavelength, absorbance and of the oil shows higher accuracy with root mean square error () of 0.1961.

摘要

监测变压器油的状况被认为是预防性维护措施之一,对于确保设备的安全和最佳性能至关重要。可以监测油的各种性质和成分,如酸值、呋喃化合物和颜色。目前的方法用于确定变压器油的颜色指数()会在测量中产生 0.5 的误差,存在人为处理误差的高风险,还会产生额外的采样和运输费用,并且由于安全和健康原因,每天只能测量有限的样本。因此,本工作提出使用紫外-可见(UV-Vis)光谱法来确定变压器油的 。结果表明,变压器油的 与油的光谱响应在 300nm 至 700nm 之间有很好的相关性。建立了模型方程来将油的 与截止波长和吸光度以及 360nm 至 600nm 区间的曲线下面积相关联。用另一组油样对这些方程进行了验证。描述油的截止波长、吸光度和 之间关系的方程具有更高的准确性,其均方根误差()为 0.1961。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b98/6069396/839245df76a9/sensors-18-02175-g001.jpg

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