Database Generation for Data-Driven Power System Security Assessment Under Uncertainty
Tian Xia, Qingchun Hou, Ning Zhang, Qihuan Dong, Weiran Li, Chongqing Kang
Abstract
High renewable penetration brings diversified operation states and complex dynamic behaviors to power systems and challenges the dynamic security assessment calculation. Data-driven methods have become increasingly important to address this challenge. However, the performance of data-driven DSA is heavily driven by the quality of the database generated for training the model, i.e., how well the database represents the operation states which need to be evaluated by the data-driven DSA. This paper proposes a database generation method that can generate samples following the probability distribution of operation states which need to be evaluated by the data-driven DSA in high renewable penetrated power system. In the method, the probability distribution of operation states which need to be evaluated by data-driven DSA in high renewable penetrated power system is modeled as probabilistic feasible region, which is a probability distribution of unnormalized PDF on convex polytope. An efficient sampling method is designed to generate operation state samples from the probability distribution of unnormalized PDF on polytope. The effectiveness of the proposed method and the improvement compared to OPF-based method, Gapsplit method, and optimization-based exploration method are demonstrated by case study on the transient angular stability problem of a 170-bus dynamic test system.