2019/12/19 by Xu Du, Du, Xu, Alexander Engelmann +7
Computer Science · Decision Sciences · Engineering · Physics and Astronomy · #Control Systems and Identification #FOS: Electrical engineering #Model Reduction and Neural Networks #Probabilistic and Robust Engineering Design #Systems and Control (eess.SY) #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1912.09017
openalex publication_date 2019/12/19 · arxiv created 2020/05/11 · arxiv updated 2020/05/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The integration of renewables into electrical grids calls for the development of tailored control schemes which in turn require reliable grid models. In many cases, the grid topology is known but the actual parameters are not exactly known. This paper proposes a new approach for online parameter estimation in power systems based on optimal experimental design using multiple measurement snapshots. In contrast to conventional methods, our method computes optimal excitations extracting the maximum information in each estimation step to accelerate convergence. The performance of the proposed method is illustrated on a case study.