A Data-Driven Scheme Based on Sparse Projection Oblique Randomer Forests for Real-Time Dynamic Security Assessment

Abstract

With the wide interconnection of power systems and extensive application of phasor measurement units (PMUs), the secure operation of power systems is facing considerable challenges. To satisfy the demand of online dynamic security assessment (DSA) for modern power systems, a data-driven scheme based on sparse projection oblique randomer forests (SPORF) is proposed, which includes offline training, periodic update and online assessment. In the first stage, an improved adaptive synthetic sampling (ADASYN) method is developed to mitigate the class imbalance problem for the data-driven DSA approach. Then, the SPORF-based DSA model is trained using crucial features with low redundancy selected by a feature selection procedure based on the minimal-redundancy-maximal-relevance (MRMR) criterion. In the second stage, the periodic update of the DSA model for unseen system topologies is executed to enhance the robustness of the model. In the third stage, the trained model can provide the DSA result immediately when the real-time operation information of a system is received. The satisfactory performance of the proposed scheme is demonstrated through a series of tests and the comparisons on a 23-bus system and a practical 1648-bus system.

Keywords

• Dynamic security assessment
• data-driven
• data oversampling
• feature selection
• sparse projection oblique randomer forests