Condition assessment based on gray clustering with cloud whiten function for power transformer

Abstract

A new gray clustering analysis algorithm, which adopted cloud model as whiten function, was proposed to solve the subjective problem in power transformer condition assessment. The algorithm determined condition classification and parameters through their prior knowledge. The algorithm first optimized indicators of power transformer condition evaluation and stratified them. Then, cloud model was introduced as whiten function of gray clustering because the combination of these two methods could comprehensively consider randomness, ambiguity and gray of uncertainty problems. The parameters of cloud models determined according to prior knowledge of power transformer condition evaluation indicators, so it reflected the power transformer condition more really and objectively. Improved weight coefficient gray target theory analyzed dissolved gases and improved the accuracy of the model. Indicators polarities of the improved weight coefficient gray target theory were optimized so that it could evaluate normal state. For samples only with dissolved gases data, improved weight coefficient gray target algorithm can assess condition alone. Example indicates that condition evaluation results of the power transformer based on gray clustering with cloud whiten function consistent with the actual situation, and raise the scientific and objective of power transformer condition evaluation. © 2011 Springer-Verlag.