DSA using synchronized phasor measurement and decision trees

Summary form only given. On-line dynamic security assessment (DSA) is examined in a data-mining framework by taking into account the operating condition (OC) variations and possible topology change of power systems during the operating horizon. Specifically, a robust scheme is proposed based on adaptive ensemble decision tree (DT) learning. In off-line 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 the variations of OCs or possible change of system topology; the voting weights of the small-height DTs are also updated accordingly. In on-line DSA, the updated classification model is used to map the real-time measurements of the current OC to security classification decisions. The proposed scheme is applied to a regional grid of the Western Electricity Coordinating Council system. The results of a case study using a variety of realistic OCs illustrate the effectiveness of the proposed scheme in dealing with OC variations and system topology change.