Abstract
The state of cellulosic solid kraft paper (CSKP) insulation, to a large extent, is an indication of a transformer’s health. It not only reflects the condition of transformer but also diagnose its residual life. The quantity of 2-furfuraldehyde (2-FAL), carbon dioxide (CO2), and carbon monoxide (CO) dissolved in the transformer oil are useful diagnostic indicators to predict the state of the CSKP insulation. In this work, the current physical state of the CSKP is determined with the help of easily measurable parameters, like temperature, moisture, and the aging time. Here, the degree of deterioration of CSKP insulation has been determined using an integrated insulation health assessment system. This technique integrates a two-stage system comprising of a neural network (NN) model followed by a Smart Life Prediction Approach (SLPA). A thermo-moisture-aging multi-layer feed-forward NN model has been developed to predict the concentrations of 2-FAL, CO2, and CO, which are further correlated to estimate the Degree of Polymerization (DP) values adopting an SLPA. The advantage of the proposed integrated system is that it provides an alternative means of paper health assessment based on Dissolved Gas Analysis (DGA) without estimating dissolved gas concentrations in oil, thereby avoiding the use of sophisticated measuring instruments. The optimal configuration of the NN model has been achieved at minimum iterations with an average cross-validation mean square error of 3.78 × 10−7. The proposed system thereby avoids destructive and offline measurement of DP and facilitates real-time condition monitoring of oil-immersed transformers. The test results of the developed system show considerable reliability in determining insulation health using easily measurable parameters. Furthermore, the system’s performance is compared with reported work and has been found to provide encouraging outcomes.
Highlights
Due to our modern lives being heavily technology dependent, it has become exceedingly important to maintain reliable electrical power supply
The IEEE C57.104 (2008) standard highlights the use of carbon oxides and other hydrocarbon fault gases in diagnosing and monitoring the insulation condition of the oil-immersed power transformers
The process involves the division of the dataset into ten groups of equal size. Nine out of these ten group is used to Processes 2021, 9, x FOR PEER REVIEWgroups are utilized for training the network while the remaining one validate the trained Artificial Neural Network (ANN) model
Summary
Due to our modern lives being heavily technology dependent, it has become exceedingly important to maintain reliable electrical power supply. Transformers, being a critical component of the power system, could severely impact the power system performance if they are left unattended or inadequately maintained. Their breakdown has far-reaching financial consequences [1,2]. This mandates to identify the transformer faults at the incipient stage, which will help to ensure its smooth operation. The transformer’s insulation system is composed of mineral oil (MO) and cellulosic solid kraft paper (CSKP). The overloading condition in the transformer produces thermal stresses, which in turn accelerates the failure of the CSKP insulation [3]
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