Fast Dynamic Voltage Security Margin Estimation: Concept and Development

Artikel i vetenskaplig tidskrift, 2020

This paper develops a machine learning-based method for 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, the paper first establishes and visualizes the circumstances when the DVSM is to prefer to the static VSM. To overcome the computational difficulties in estimating the DVSM, the paper 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 approximately 70 %, allowing system operators to perform the estimations in near real-time.