Open AccessHealth condition assessment of wind turbine generators based on supervisory control and data acquisition data
Author(s) -
Li Junqing,
Li Qiujia,
Zhu Jianguo
Publication year - 2019
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2018.5504
Subject(s) - steam turbine , reliability engineering , turbine , condition monitoring , fuzzy logic , control theory (sociology) , computer science , engineering , control engineering , control (management) , mechanical engineering , electrical engineering , artificial intelligence
As the running time of a wind turbine generator unit (WTGU) increases, the ageing and wear of its components will be aggravated gradually, which leads to deterioration of its operation condition. In order to ensure the safe and stable operation of WTGU and prolong its service life, it is of great significance to know the health condition of the WTGU and reasonably arrange maintenance. Based on the structure of the WTGU and the operation principle of the wind turbine generator (WTG), the condition assessment indexes of WTG were built, and the factors influencing the assessment index were determined. The relationship function between the assessment indexes and their influencing factors was established by using the least square support vector machine, which was used to determine the dynamic limitation of condition assessment index of WTG. The dynamic weight of each assessment index was used to characterise the influence of deterioration degree of different components on WTG conditions. Then the health conditions of WTG were evaluated using a similar cloud and fuzzy comprehensive assessment method separately. Finally, the proposed methods were verified by two examples including a direct‐drive permanent magnet wind generator and a doubly‐fed wind generator.