A good point set-based knowledgebase generation scheme for power system intelligent dynamic security assessment

The increased penetration of wind power generation has introduced significant uncertainty and complexity to power system operations, making the real-time dynamic security assessment (DSA) a necessity to protect the system against the risk of blackouts. As a promising strategy for real-time DSA, the intelligent system (IS)-based approach extracts the DSA knowledge from a dynamic security knowledgebase (KB). The quality of KB is the key to the success of such an IS. The conventional Monte Carlo (MC) method requires a large number of sampled cases to cover the operating point (OP) space due to its random sampling mechanism, and therefore is computationally expensive. To generate a KB more efficiently, this paper proposes a Good Point Set (GPS)-based KB generation scheme where the sample distribution is more uniform. To achieve the same level of DSA accuracy, the GPS-based approach requires less number of samples. A case study is conducted on power system with high wind penetration and its result verifies that the GPS method outperforms conventional MC method.