Moisture Diagnosis of Transformer Oil-Immersed Insulation With Intelligent Technique and Frequency-Domain Spectroscopy

Jiefeng Liu, Xianhao Fan, Chaohai Zhang, Chun Sing Lai, Yiyi Zhang, Hanbo Zheng, Loi Lei Lai, Enze Zhang

Abstract: Moisture is one of the critical factors to determine the service life of transformers. The moisture inside the transformer oil-immersed insulation could be quantified with feature parameters. This article proposes and develops a genetic algorithm support vector machine (GA-SVM) model to carry out the moisture diagnosis. Present findings reveal that these feature parameters can be obtained by using frequency-domain spectroscopy. Therefore, a novel model for predicting the frequency-domain spectroscopy curves is first reported based on a small number of samples, which could be utilized to obtain the feature parameters database to develop GA-SVM. Then, the moisture diagnosis in the lab and field conditions is presented to verify its feasibility and accuracy. The novelty of this article is in an exploration of the reported model as an intelligent based moisture diagnosis tool for power transformers.