Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers

Abstract

This paper proposes a novel algorithm based on an advanced feature selection technique for the decision tree (DT) classifier to assess the dynamic security in a power system. The proposed methodology utilizes symmetrical uncertainty (SU) to reduce the data redundancy in a dataset for DT classifier-based dynamic security assessment (DSA) tools. The results show that SU reduces the dimension of the dataset used for DSA significantly. Subsequently, the approach improves the performance of the DT classifier. The effectiveness of the proposed technique is demonstrated on the modified IEEE 30-bus test system model. The results show that the DT classifier with SU outperforms the DT classifier without SU. The performance of the proposed algorithm indicates that the DT classifier with SU is able to assess the dynamic security of the system in near real time. Therefore, it is able to provide vital information for protection and control applications in power system operation.