A quasi-online condition monitoring technique for the wind power converter

• Published in: International journal of electrical power & energy systems Vol. 130; p. 106971
• Main Authors: Zhang, Jun, Du, Xiong, Qian, Cheng, Tai, Heng-Ming
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2021
• Subjects: Condition monitoring; Cooling system; Power module; Quasi-online technique; Wind power converter; Condition monitoring; Wind power converter; Power module; Cooling system; Quasi-online technique
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2021.106971

•A quasi-online condition monitoring technique for wind power converter is proposed.•This method enables concurrent health monitoring of power module and cooling system.•This approach does not need to measure the power loss and junction temperature. The power converter plays an important role in the energy conversion and management of wind turbine system. It is one of the core components in the system, but also one of the most prone to fail components according to industry-wide survey. Condition monitoring of power converter can improve the reliable operation of wind turbine system and reduce its unscheduled downtime. In this paper, a novel quasi-online monitoring technique based on the time-constants of heat sink temperature is proposed to check the health status of wind power converter. Advantages of the proposed technique include: (1) This approach enables the monitoring of power module degradation and cooling system deterioration in the converter simultaneously. (2) This method does not need to measure the junction temperature and power loss which reduces the cost and complexity of operation. Experimental tests were conducted to verify the effectiveness of this technique. Results also indicate that the maximum coefficient of variation of time-constants is below 1.7% under different converter operating conditions and ambient temperatures. In addition, the maximum error of time-constants in the application of condition monitoring is less than 3.1%.