Study on the Application of Similar Clustering Approach in Wind Turbine Fault Detection

• Main Authors: Pei-Chun, Hsieh, 謝佩鈞
• Other Authors: 蔡進發
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/823apz

碩士 === 國立臺灣大學 === 工程科學及海洋工程學研究所 === 105 === A method of fault detection based on similar clustering approach for the wind turbines within a wind farm is proposed in this study. The SCADA datas were used for the clustering and fault detection. The wind speed and wind direction were used for the clustering approach. The Gaussain distribution was derived for each turbine with the parameters of wind speed and wind direction. Then the confidence values (CV) were calculated between turbines based on the Gaussian distribution of wind speed and direction. The turbines were clustered by the hierarchical clustering method with the value of 1-CV. Then the Gaussian distributions of power, rotor speed, pitch angle of balde and yawing misalightment were calculated within the same cluster. The confidence value of these parameters between turbines were used to figure out the abnormal operation turbine. The SCADA datas of 23 wind turbines of Taipower which is located in Chang Hua Costal Industry Park were used for the analysis in this study. The turbines can be clustered into two groups. The first group has the wind direction from the north and the second group has the wid direction from northeast based on the wind datas from SACDA. The fault analysis shows that the No. 22 wind turbine of group 1 had most abnormal operations and the No.7 wind turbine of group 1 had no abnormal operation. It also shows that the No. 4 wind turbine of group 2 had most abnormal operations and the No. 12 wind turbine of group 2 operated smoothly without any abnormal operation. It is found that the confidence value of power is smaller than 0.5 when the confidence value of pitch angle is smaller than 0.6. And the confidemce value of power is low when the confidence value of the yawing misalightment is smaller than 0.5.