Planning Sensitivities for Building Contingency Robustness and Graph Properties into Large Synthetic Grids

Interest in promoting innovation for large, high-voltage power grids has driven recent efforts to reproduce actual system properties in synthetic electric grids, which are fictitious datasets designed to be large, complex, realistic, and totally public. This paper presents new techniques based on system planning sensitivities, integrated into a synthesis methodology to mimic the constraints used in designing actual grids. This approach improves on previous work by explicitly quantifying each candidate transmission line’s contribution to contingency robustness, balancing that with geographic and topological metrics. Example synthetic grids build with this method are compared to actual transmission grids, showing that the emulated careful design also achieves observed complex network properties. The results shed light on how the underlying graph structure of power grids reflects the engineering requirements of their design. Moreover, the datasets synthesized here provide researchers in many fields with public power system test cases that are detailed and realistic

Predicting Cascading Failures in Power Grids using Machine Learning Algorithms

Although there has been notable progress in modeling cascading failures in power grids, few works included using machine learning algorithms. In this paper, cascading failures that lead to massive blackouts in power grids are predicted and classified into no, small, and large cascades using machine learning algorithms. Cascading-failure data is generated using a cascading failure simulator framework developed earlier. The data set includes the power grid operating parameters such as loading level, level of load shedding, the capacity of the failed lines, and the topological parameters such as edge betweenness centrality and the average shortest distance for numerous combinations of two transmission line failures as features. Then several machine learning algorithms are used to classify cascading failures. Further, linear regression is used to predict the number of failed transmission lines and the amount of load shedding during a cascade based on initial feature values. This data-driven technique can be used to generate cascading failure data set for any real-world power grids and hence, power-grid engineers can use this approach for cascade data generation and hence predicting vulnerabilities and enhancing robustness of the grid