Fault identification of wind turbine drivetrain using neural network based on gravitational search algorithm
LIU Yong-qian1, XU Qiang1, David INFIELD1,2, TIAN De1, LONG Quan3
1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;2. Institute of Energy and Environment, University of Strathclyde, Glasgow G1 1XW, UK;3. Test and Research Institute, China Datang Corporation Renewable Power Co. , Ltd. , Beijing 100068, China
Fault identification of wind turbine drivetrain is the key for wind farms to make appropriate maintenance strategies to reduce the downtime and maintenance cost, and also one of the highly discussed issues and difficulties in recent research. Gravitational search algorithm was applied in the optimization of the initial weights and thresholds of BP neural network for the first time. Therefore, a fault identification method using BP neural network based on gravitational search algorithm was proposed and applied in wind turbine drivetrain. Tests showed that the presented method could precisely identify three typical wind turbine drivetrain faults, which were gear wear, tooth breaking and bearing looseness respectively, with higher average accuracy than BP neural network, so the effectiveness of the proposed method is verified.
刘永前;徐 强;David Infield;田 德;龙 泉. 基于引力搜索神经网络的风电机组传动链故障识别[J]. 振动与冲击, 2015, 34(2): 134-137.
LIU Yong-qian;XU Qiang;David INFIELD;TIAN De;LONG Quan . Fault identification of wind turbine drivetrain using neural network based on gravitational search algorithm. JOURNAL OF VIBRATION AND SHOCK, 2015, 34(2): 134-137.