TY - JOUR
T1 - Robust online dynamic security assessment using adaptive ensemble decision-tree learning
AU - He, Miao
AU - Zhang, Junshan
AU - Vittal, Vijay
PY - 2013
Y1 - 2013
N2 - Online dynamic security assessment (DSA) is examined in a data-mining framework by taking into account the operating condition (OC) variations and possible topology changes of power systems during the operating horizon. Specifically, a robust scheme is proposed based on adaptive ensemble decision tree (DT) learning. In offline training, a boosting algorithm is employed to build a classification model as a weighted voting of multiple unpruned small-height DTs. Then, the small-height DTs are periodically updated by incorporating new training cases that account for OC variations or the possible changes of system topology; the voting weights of the small-height DTs are also updated accordingly. In online DSA, the updated classification model is used to map the real-time measurements of the present OC to security classification decisions. The proposed scheme is first illustrated on the IEEE 39-bus test system, and then applied to a regional grid of the Western Electricity Coordinating Council (WECC) system. The results of case studies, using a variety of realized OCs, illustrate the effectiveness of the proposed scheme in dealing with OC variation and system topology change.
AB - Online dynamic security assessment (DSA) is examined in a data-mining framework by taking into account the operating condition (OC) variations and possible topology changes of power systems during the operating horizon. Specifically, a robust scheme is proposed based on adaptive ensemble decision tree (DT) learning. In offline training, a boosting algorithm is employed to build a classification model as a weighted voting of multiple unpruned small-height DTs. Then, the small-height DTs are periodically updated by incorporating new training cases that account for OC variations or the possible changes of system topology; the voting weights of the small-height DTs are also updated accordingly. In online DSA, the updated classification model is used to map the real-time measurements of the present OC to security classification decisions. The proposed scheme is first illustrated on the IEEE 39-bus test system, and then applied to a regional grid of the Western Electricity Coordinating Council (WECC) system. The results of case studies, using a variety of realized OCs, illustrate the effectiveness of the proposed scheme in dealing with OC variation and system topology change.
KW - Boosting
KW - Data mining
KW - Decision tree
KW - Ensemble learning
KW - Online dynamic security assessment
KW - Transient stability
UR - http://www.scopus.com/inward/record.url?scp=84886102463&partnerID=8YFLogxK
U2 - 10.1109/TPWRS.2013.2266617
DO - 10.1109/TPWRS.2013.2266617
M3 - Article
AN - SCOPUS:84886102463
SN - 0885-8950
VL - 28
SP - 4089
EP - 4098
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
IS - 4
ER -