Energies 2012, 5(1), 157-167; doi:10.3390/en5010157
Article

A Two-Dimensional Cloud Model for Condition Assessment of HVDC Converter Transformers

Received: 18 November 2011; in revised form: 17 January 2012 / Accepted: 17 January 2012 / Published: 23 January 2012
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Converter transformers are the key and the most important components in high voltage direct current (HVDC) power transmission systems. Statistics show that the failure rate of HVDC converter transformers is approximately twice of that of transformers in AC power systems. This paper presents an approach integrated with a two-dimensional cloud model and an entropy-based weight model to evaluate the condition of HVDC converter transformers. The integrated approach can describe the complexity of HVDC converter transformers and achieve an effective assessment of their condition. Data from electrical testing, DGA, oil testing, and visual inspection were chosen to form the double-level assessment index system. Analysis results show that the integrated approach is capable of providing a relevant and effective assessment which in turn, provides valuable information for the maintenance of HVDC converter transformers.
Keywords: HVDC converter transformer; condition assessment; cloud model; entropy weight
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MDPI and ACS Style

Li, J.; He, Z.; Wang, Y.; Lv, J.; Zhao, L. A Two-Dimensional Cloud Model for Condition Assessment of HVDC Converter Transformers. Energies 2012, 5, 157-167.

AMA Style

Li J, He Z, Wang Y, Lv J, Zhao L. A Two-Dimensional Cloud Model for Condition Assessment of HVDC Converter Transformers. Energies. 2012; 5(1):157-167.

Chicago/Turabian Style

Li, Jian; He, Zhiman; Wang, Youyuan; Lv, Jinzhuang; Zhao, Linjie. 2012. "A Two-Dimensional Cloud Model for Condition Assessment of HVDC Converter Transformers." Energies 5, no. 1: 157-167.

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