Rapid Transformer Health State Recognition Through Canopy Cluster-Merging of Dissolved Gas Data in High-Dimensional Space

Abstract

Dissolved gases in oil are major parameters for assessing the health state of power transformers. Recognizing that the existing state recognition methods are often restrained by data fluctuation and usually require greater computational load, this paper proposes a rapid transformer health state recognition method through Canopy cluster-merging of dissolved gas data in high-dimensional space. Following the introduction of fluctuation coefficient to evaluate the quality of data and the assignation of weight to reflect the difference between gases, the variable-weighted high-dimensional space of dissolved gases is established to suppress the impact from the fluctuated data. The novel Canopy cluster-merging method that overcomes the instability and high computational complexity of the conventional clustering method is then proposed and used in the variable-weighted high-dimensional space to recognize the abnormal state transformer. Applying the state recognition rules and matching with the established abnormal event base, the health state of the transformer could be rapidly recognized. The single case verification concludes that the proposed method has better clustering effect and can significantly improve the clustering speed even by 17.08 times. The group verification test indicates that the proposed method not only demonstrates an accuracy as high as 91.43% but also shows extremely high efficiency compared with the conventional recognition methods.

Keywords

• Transformers
• health state recognition
• Canopy cluster-merging algorithm
• variable-weighted high-dimensional space