Data-Driven Fault Diagnosis Method for Power Transformers Using Modified Kriging Model. (22nd October 2017)

• Record Type: Journal Article
• Authors: Ding, Yu; Liu, Qiang
• Other Names: Liu Wanquan Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2017(2017)
• Publication Date: 2017-10-22
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2017/3068548
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : A data-driven fault diagnosis method that combines Kriging model and neural network is presented and is further used for power transformers based on analysis of dissolved gases in oil. In order to improve modeling accuracy of Kriging model, a modified model that replaces the global model of Kriging model with BP neural network is presented and is further extended using linearity weighted aggregation method. The presented method integrates characteristics of the global approximation of the neural network technology and the localized departure of the Kriging model, which improves modeling accuracy. Finally, the validity of this method is demonstrated by several numerical computations of transformer fault diagnosis problems.