Pattern recognition techniques for power transformer insulation diagnosis—a comparative study part 2: implementation, case study, and statistical analysis

Summary Transformer oil tests such as breakdown voltage, resistivity, dielectric dissipation factor, water content, 2‐furfuraldehyde, acidity, and different dissolved gasses have been adopted in utility companies for evaluating the conditions of transformer insulation. Over the past 20 years, various pattern recognition techniques have been applied for power transformer insulation diagnosis using oil tests results (oil characteristics). This paper investigates a variety of state‐of‐the‐art pattern recognition algorithms for transformer insulation diagnosis. To verify the applicability and generalization capability of different pattern recognition algorithms, this paper implements 15 representative algorithms and conducts extensive case studies on eight oil characteristics datasets collected from different utility companies. A statistical performance (in terms of classification accuracy) comparison among different pattern recognition algorithms for transformer insulation diagnosis using oil characteristics is also conducted in the paper. Copyright © 2014 John Wiley & Sons, Ltd.