Data‐driven transient frequency stability assessment: A deep learning method with combined estimation‐correction framework
Yunfeng Wen, Rongzhen Zhao, Mingzeng Huang, Chuangxin Guo
Abstract
Abstract Transient frequency stability assessment (TFSA) is important for operators to understand dynamic power system frequency characteristics. However, because of the problem of combinatorial explosion, exacerbated by uncertain renewable generation and various contingencies, applying traditional time‐domain simulation methods is very time‐consuming for TFSA of a low‐inertia power system. To address this, this study evaluates a fast and online TFSA using a data‐driven tool, based on deep learning. A novel combined estimation–correction learning framework is proposed. In the estimation step, a deep neural network is used to obtain the network parameters based on actual input–output feature data, which effectively realize automatic dimension reduction and feature extraction. In the subsequent correction step, the multidimensional frequency metrics produced by the deep neural network‐based estimator are input to a stacked extreme learning machine‐based corrector to further reduce the prediction error. A case study on a modified IEEE RTS‐79 system demonstrates that the proposed approach has extremely high computation speed, compared to the time‐domain simulation method, and achieves higher prediction accuracy and superior generalisation performance in comparison with other state‐of‐the‐art algorithms.