Enhancing credibility of transient stability assessment under stochastic operation modes in power systems: An incremental dataset recognition approach

With the increasing penetration of new energy sources in power systems, preliminary security verification by transient stability assessment (TSA) under typical operation modes gradually becomes inadequate. Hence this paper introduces an incremental dataset construction method for efficient TSA model update, which aims at recognizing the cases composing out-of-scope region and boundary region where TSA models generate incredible results. Firstly, a composite distance metric integrating value-based and shape-based similarities of transient response is put forward to identify the local sample space. The cases possessing low membership to the space are classified as outliers. Secondly, an improved localized generalization error estimation (ILGEE) algorithm is originally proposed for variance upper bound estimation of worst TSA error, and furtherly the error is modeled as a Gaussian distribution incorporating Neumann boundary condition. The scene-specific credibility index (SSCI) is then defined such that TSA conditions corresponding to high SSCI are categorized as boundary cases. Finally, the TSA model could be fine-tuned with the incredible out-of-scope and boundary cases labeled by time domain simulation. Case study on a simplified provincial power grid verifies TSA accuracy enhancement (97.58% to 98.35%) and credibility improvement (critical SSCI from 0.93 to 0.67) within 36.7% incremental time by the proposed scheme.