Data Mining with Improved Apriori Algorithm on Wind Generator Alarm Data
Alarm system is an important subsystem of Supervisory Control and Data Acquisition (SCADA) system in wind turbines.Because of the bad operating environment and condition switching frequently,this system has the problems of alarm too frequently and too large amount of alarm data which greatly reduce the effectiveness of the alarm system.Remove the excessive redundant alarm and refine the valid information are significant to early find wind turbines abnormal operation,to research the causal connection between different faults and to reduce the operators’ workload.Data mining is an effective way to solve this problem.This paper used association rules on improved Apriori algorithm to analysis the alarm information which happened before and after blade angle asymmetry fault.Combined with the running mechanism of variable-pitch systems,we find the implied causal relationships between faults,then filter out minor redundant information,refine effective leading fault alarms and at last greatly reduce the number of alarm,improve the operators’ work efficiency.
Data Mining Fault Alarm Pitch Control Fault Condition Monitoring
Chao Tong Peng Guo
School of Control and Computer Engineering, North China Electric Power University, Changping, Beijing 102206
国际会议
the 25th Chinese Control and Decision Conference(第25届中国控制与决策会议)
贵阳
英文
1936-1941
2013-05-01（万方平台首次上网日期，不代表论文的发表时间）