Comparative studies of power grid security with network connectivity and power flow information using unsupervised learning
Date of Original Version
The modern electric power grid has become highly integrated in order to increase reliability of power transmission from the generating units to end consumers. This integrated nature and its upgrade toward an intelligent smart grid make the power grid vulnerable when facing cyber or physical attacks as well as intentional attacks. Therefore, determining the most vulnerable components (e.g., buses or generators) is critically important for power grid defense. In this paper, a new definition of load is proposed by taking power flow into consideration in comparison with the load definition based on degree or network connectivity. Unsupervised learning techniques (e.g., K-means algorithm and self-organizing map (SOM)) are introduced to cluster the nodes (i.e., buses) in IEEE-39 bus and IEEE-57 bus benchmarks. Then most vulnerable node in each cluster is determined based on their load information to form initial victim set. We use percentage of failure (PoF) to compare the performance of clustering based approach and traditional load based approach during cascading failure process. With the simulation results, the unsupervised learning (clustering based) approaches are more efficient in finding the most vulnerable nodes and our proposed definition of load is relatively useful in studying power grid security.
Proceedings of the International Joint Conference on Neural Networks
Poudel, Shiva, Zhen Ni, Xiangnan Zhong, and Haibo He. "Comparative studies of power grid security with network connectivity and power flow information using unsupervised learning." Proceedings of the International Joint Conference on Neural Networks 2016-October, (2016): 2730-2737. doi:10.1109/IJCNN.2016.7727542.