Online Dynamic Security Assessment With Missing PMU Measurements: A Data Mining Approach

A data mining approach using ensemble decision trees (DTs) learning is proposed for online dynamic security assessment (DSA), with the objective of mitigating the impact of possibly missing PMU data. Specifically, multiple small DTs are first trained offline using a random subspace method. In particular, the developed random subspace method exploits the hierarchy of wide-area monitoring system (WAMS), the locational information of attributes, and the availability of PMU measurements, so as to improve the overall robustness of the ensemble to missing data. Then, the performance of the trained small DTs is re-checked by using new cases in near real-time. In online DSA, viable small DTs are identified in case of missing PMU data, and a boosting algorithm is employed to quantify the voting weights of viable small DTs. The security classification decision for online DSA is obtained via a weighted voting of viable small DTs. A case study using the IEEE 39-bus system demonstrates the effectiveness of the proposed approach.