Fast dynamic voltage security margin estimation: concept and development. Issue 4 (29th May 2020) - Hagmar, Hannes; Eriksson, Robert; Tuan, Le Anh
Abstract : This study develops a machine learning‐based method for a fast estimation of the dynamic voltage security margin (DVSM). The DVSM can incorporate the dynamic system response following a disturbance and it generally provides a better measure of security than the more commonly used static voltage security margin (VSM). Using the concept of transient P ‐ V curves, this study first establishes and visualises the circumstances when the DVSM is to prefer the static VSM. To overcome the computational difficulties in estimating the DVSM, this study proposes a method based on training two separate neural networks on a data set composed of combinations of different operating conditions and contingency scenarios generated using time‐domain simulations. The trained neural networks are used to improve the search algorithm and significantly increase the computational efficiency in estimating the DVSM. The machine learning‐based approach is thus applied to support the estimation of the DVSM, while the actual margin is validated using time‐domain simulations. The proposed method was tested on the Nordic32 test system and the number of time‐domain simulations was possible to reduce with ∼70%, allowing system operators to perform the estimations in near real‐time.