Odor recognition of deteriorated mineral oils using an odor-sensing array

Abstract

The deterioration or oxidation of the mineral oil in transformers poses the risk of short circuits. Convenient and effective methods are expected to be developed. Carbon-based sensor arrays were used in this study to identify the quality variations of mineral oil for oil-filled transformers by odors. The sensitive layers of the odor-sensing system consisted of different types of GC stationary phase materials and carbon black (CB) mixtures. We made a targeted selection of GC materials by utilizing the polarities to make a sensor array based on the distinct components of mineral oil such as alkanes and xylenes by gas chromatography mass spectrometry (GC/MS) analysis. The response characteristics of the sensitive layers were used to recognize the mineral oil odors by machine learning. With laboratory air as the carrier gas, the system could distinguish mineral oil that has been in use for over 20 years from new mineral oil with an accuracy of about 93.8%. The identification accuracy achieved was about 60% for three different concentrations of unused mineral oil and the oxidized mineral oil created by the transformer’s leakage. When detecting the oxidized mineral oil with a concentration of more than 50%, the accuracy rate reached more than 80%. The odor-sensing system in this study will help inspect mineral oils in the transformer and make leakage judgments in a short time.