TY - GEN
T1 - The impact of mission profile models on the predicted lifetime of IGBT modules in the modular multilevel converter
AU - Zhang, Yi
AU - Wang, Huai
AU - Wang, Zhongxu
AU - Yang, Yongheng
AU - Blaabjerg, Frede
N1 - Funding Information:
ACKNOWLEDGMENT The authors gratefully acknowledge the provided wind data from BMWi (Bundesministerium fuer Wirtschaft und Energie, Federal Ministry for Economic Affairs and Energy ) and the PTJ (Projekttraeger Juelich, project executing organisation), as well as the financial support from China Scholarship Council.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/15
Y1 - 2017/12/15
N2 - The reliability aspect study of Modular Multilevel Converter (MMC) is of great interest in industry applications, such as offshore wind. Lifetime prediction of key components is an important tool to design MMC with fulfilled reliability specifications. While many efforts have been made to the lifetime prediction of IGBT modules in renewable energy applications by considering long-term varying operation conditions (i.e., mission profile), the justifications of using the associated mission profiles are still missed. This paper investigates the impact of mission profile data resolutions and electrical power modeling methods on the estimated lifetime of IGBT modules in an MMC for offshore wind power application. In a 30 MW MMC case study, an annual wind speed profile with a resolution of 1 s/data, 10 minute/data, and 1 hour/data are considered, respectively. A method to re-generate higher resolution wind speed data from lower resolution data is introduced as well. Based on the wind speed data, IEC 61400-12-1 power curve model and a wind speed-power stochastic model are compared as well. Five mission profile modeling scenarios are compared in terms of the predicted lifetime of the IGBT modules used in the MMC, resulting in significant differences. The study serves as a first step to quantify the impact of mission profile modeling on lifetime prediction, and to provide a guideline on mission profile collection for the presented application.
AB - The reliability aspect study of Modular Multilevel Converter (MMC) is of great interest in industry applications, such as offshore wind. Lifetime prediction of key components is an important tool to design MMC with fulfilled reliability specifications. While many efforts have been made to the lifetime prediction of IGBT modules in renewable energy applications by considering long-term varying operation conditions (i.e., mission profile), the justifications of using the associated mission profiles are still missed. This paper investigates the impact of mission profile data resolutions and electrical power modeling methods on the estimated lifetime of IGBT modules in an MMC for offshore wind power application. In a 30 MW MMC case study, an annual wind speed profile with a resolution of 1 s/data, 10 minute/data, and 1 hour/data are considered, respectively. A method to re-generate higher resolution wind speed data from lower resolution data is introduced as well. Based on the wind speed data, IEC 61400-12-1 power curve model and a wind speed-power stochastic model are compared as well. Five mission profile modeling scenarios are compared in terms of the predicted lifetime of the IGBT modules used in the MMC, resulting in significant differences. The study serves as a first step to quantify the impact of mission profile modeling on lifetime prediction, and to provide a guideline on mission profile collection for the presented application.
UR - http://www.scopus.com/inward/record.url?scp=85046682637&partnerID=8YFLogxK
U2 - 10.1109/IECON.2017.8217399
DO - 10.1109/IECON.2017.8217399
M3 - Conference contribution
AN - SCOPUS:85046682637
T3 - Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
SP - 7980
EP - 7985
BT - Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017
Y2 - 29 October 2017 through 1 November 2017
ER -
