Application of Infrared Spectroscopy in Oil Quality Detection

Qian, Yihua; Wang, Yan; Zhao, Yongxiang; Fan, Shengping; Fu, Qiang; Wang, Hongmei

doi:10.1109/ichve49031.2020.9279670

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…Table 1: Modified Oil Quality Index [18,19] . MATLAB was used to develop the machine learning model using five (5) fold cross validation to protect against overfitting. The test data is a 7 × 5 dataset from existing laboratory experiment [20] consisting of five ( 5) different age classes.…”

Section: Classification Of Service-aged Insulating Oilmentioning

confidence: 99%

“…Insulation ageing is the gradual, irreversible deterioration of the physicochemical properties of an insulator in service due to electrical, thermal, and environmental stresses. During operation, transformer oil degrades based on the transformer's loading condition and thermal stress [4,5]. The thermal stress originates from either the copper or iron core losses of the windings.…”

Section: Introductionmentioning

confidence: 99%

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Towards Intrusive Non-Destructive Online Ageing Detection of Transformer Oil Leveraging Bootsrapped Machine Learning Models

Elele

Fofana²,

Nekahi

et al. 2023

2023 IEEE Electrical Insulation Conference (EIC)

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Transformers play a crucial role in power networks, ensuring that generated electricity is delivered to consumers at the safest voltage level, reducing losses, and enabling metering and grounding. The insulation system is critical for ensuring that the function of the power transformer is carried out safely, at the expense of its gradual deterioration over time. Most conventional oil ageing detection methods are offline and, as a result, are best suited for scheduled maintenance practices which cause risk to life, sample contamination, loss of man-hour, and risk of missing critical incipient ageing responses outside the maintenance cycle window. Oil quality index (OQIN) data sets were bootstrapped to develop robust machine learning models for online ageing classification (class A to class G). Correlation models from existing mineral oil dataset was develop to predict the refractive index (RI), breakdown voltage value (BDV), dielectric dissipation factor (DDF), dissolved decayed products (DDP), total acid number (TAN), interfacial tension (IFT), and oil quality index (OQIN) using the optical fibre sensor transduced output voltage (OFSTOV) of intensity modulated optical fibre. The existing mineral oil dataset also validates the developed OQIN machine learning model. The high correlative models presented in this paper have the potential to enable the transition from traditional offline scheduled maintenance ageing detection methods to online/IoT-based prescriptive ageing detection solutions. This paper also lays the groundwork for revolutionising in-situ transformer oil ageing detection to include digital-twinning capability, thereby improving reliability, reducing risks, and reducing operational costs.

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Section: Classification Of Service-aged Insulating Oilmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Intrusive Non-Destructive Online Ageing Detection of Transformer Oil Leveraging Bootsrapped Machine Learning Models

Elele

Fofana²,

Nekahi

et al. 2023

2023 IEEE Electrical Insulation Conference (EIC)

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show abstract

“…Insulation ageing is the gradual decrease in the dielectric strength of an insulator in operation to a complete insulation breakdown from electrical and environmental stresses. While in use, transformer oil is subject to degradation that depends on the transformer loading condition/thermal stress [ 7 , 8 ]. The thermal stress originates from either the windings’ copper or core iron losses.…”

Section: Introductionmentioning

confidence: 99%

Towards Online Ageing Detection in Transformer Oil: A Review

Elele

Nekahi

Ali

et al. 2022

Sensors

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Transformers play an essential role in power networks, ensuring that generated power gets to consumers at the safest voltage level. However, they are prone to insulation failure from ageing, which has fatal and economic consequences if left undetected or unattended. Traditional detection methods are based on scheduled maintenance practices that often involve taking samples from in situ transformers and analysing them in laboratories using several techniques. This conventional method exposes the engineer performing the test to hazards, requires specialised training, and does not guarantee reliable results because samples can be contaminated during collection and transportation. This paper reviews the transformer oil types and some traditional ageing detection methods, including breakdown voltage (BDV), spectroscopy, dissolved gas analysis, total acid number, interfacial tension, and corresponding regulating standards. In addition, a review of sensors, technologies to improve the reliability of online ageing detection, and related online transformer ageing systems is covered in this work. A non-destructive online ageing detection method for in situ transformer oil is a better alternative to the traditional offline detection method. Moreover, when combined with the Internet of Things (IoT) and artificial intelligence, a prescriptive maintenance solution emerges, offering more advantages and robustness than offline preventive maintenance approaches.

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“…The detection process of the above two methods is relatively complex, and it is necessary to combine other methods to complete the detection. Infrared (IR) spectroscopy is a quantitative and qualitative analysis technique with high sensitivity, fast detection speed, and convenient operation, 29 which is used to identify edible oils such as olive oil and axseed oil. 30,31 Colloidal crystals are ordered arrays assembled from monodisperse colloidal particles, and their properties depend on the composition, particle size, and gap ller of the colloidal particles.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%