Perpustakaan Digital ITB

The condition of the oil-impregnated paper is an essential point of life diagnostics for a power transformer. The degree of polymerization (DP) of paper insulation is considered a good indicator of a determined deterioration level of insulation paper that indicates the remnant life of the transformer. In these years, researchers have been able to implement classification analysis methods on a power transformer through a database of measurement data. Further studies related to the development of machine learning for the assessment of power transformers must be accomplished in order to formulate a comprehensive model. In this master thesis, the objective is to develop a reliable algorithm to determine the current state of the oil-paper insulation based on the monitoring characteristics. With nominal classification base and numerically base using Fuzzy Inference System (FIS) and Back Propagation Neural Network (BPNN), and study its behavior. Both methods evaluate dielectric characteristic parameters, i.e., Acidity and Interfacial Tensile (IFT), of the insulating oil, and Dissolved Gas Analysis (DGA) measurement results; the concentration of carbon monoxide (CO) and carbon dioxide (CO2), and four possible combinations variable input. The results state that the estimation method by using the classification data input variable gives a better estimation outcome compared to the numerical data input. It can be seen in the performance of the FIS method is better in the evaluation based on the deviation/error value and the accuracy in the class estimation. In contrast, the BPNN tends to have a wider deviation value. The comparison shows that, in addition to Furan content-based DP estimations, both models can be used to predict the value of DP accurately and to improve the reliability of the result.