TY - GEN
T1 - A semi-supervised anomaly detection method for wind farm power data preprocessing
AU - Zhou, Yifan
AU - Hu, Wei
AU - Min, Yong
AU - Zheng, Le
AU - Liu, Baisi
AU - Yu, Rui
AU - Dong, Yu
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/1/29
Y1 - 2018/1/29
N2 - Wind farm power data is an essential data source for characteristics analysis of wind farms, and its precision highly influences the operation and control of wind power. Existing methods for abnormal wind power data detection are mainly based on supervised or unsupervised algorithms, which may suffer from huge effort consumption on artificial label-setting or improvable detection precision respectively. In this paper, a semi-supervised anomaly detection method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed. Supervision from a limited number of labelled data is utilized to guide the anomaly detection process. Simulation based on realistic wind farm power data from China is performed to verify the method validity. The simulation results illustrate that the proposed method can obtain a satisfactory performance on avoiding false dismissals and false alarms among irregular-shaped wind power clusters, as well as an improved distinguishing ability around the confusing boundaries between normal and abnormal data.
AB - Wind farm power data is an essential data source for characteristics analysis of wind farms, and its precision highly influences the operation and control of wind power. Existing methods for abnormal wind power data detection are mainly based on supervised or unsupervised algorithms, which may suffer from huge effort consumption on artificial label-setting or improvable detection precision respectively. In this paper, a semi-supervised anomaly detection method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed. Supervision from a limited number of labelled data is utilized to guide the anomaly detection process. Simulation based on realistic wind farm power data from China is performed to verify the method validity. The simulation results illustrate that the proposed method can obtain a satisfactory performance on avoiding false dismissals and false alarms among irregular-shaped wind power clusters, as well as an improved distinguishing ability around the confusing boundaries between normal and abnormal data.
KW - Anomaly detection
KW - Data mining
KW - Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
KW - Semi-supervised learning
KW - Wind farm power data preprocessing
UR - https://www.scopus.com/pages/publications/85046363498
U2 - 10.1109/PESGM.2017.8273883
DO - 10.1109/PESGM.2017.8273883
M3 - Conference contribution
AN - SCOPUS:85046363498
T3 - IEEE Power and Energy Society General Meeting
SP - 1
EP - 5
BT - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017
PB - IEEE Computer Society
T2 - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017
Y2 - 16 July 2017 through 20 July 2017
ER -