Cui Y., Ma, H. and Saha, T. (2016) Multi-source information fusion for power transformer condition assessment. Power and Energy Society General Meeting. Boston, 17-21, July 2016, 1-5.

has been cited by the following article:

• TITLE: Large Power Transformer Fault Diagnosis and Prognostic Based on DBNC and D-S Evidence Theory

  AUTHORS: Gang Li, Changhai Yu, Hui Fan, Shuguo Gao, Yu Song, Yunpeng Liu

  KEYWORDS: Power Transformer, Prognostic and Health Management (PHM), Deep Belief Network Classifier (DBNC), D-S Evidence Theory

  JOURNAL NAME: Energy and Power Engineering, Vol.9 No.4B, April 6, 2017

  ABSTRACT: Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operation of the whole power system. Due to the complex structure of the transformer, the use of single information for condition-based maintenance (CBM) has certain limitations, with the help of advanced sensor monitoring and information fusion technology, multi-source information is applied to the prognostic and health management (PHM) of power transformer, which is an important way to realize the CBM of power transformer. This paper presents a method which combine deep belief network classifier (DBNC) and D-S evidence theory, and it is applied to the PHM of the large power transformer. The experimental results show that the proposed method has a high correct rate of fault diagnosis for the power transformer with a large number of multi-source data.