Fast Dynamic Voltage Security Margin Estimation: Concept and Development
Reviewartikel, 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.

Neural Networks

Voltage security assessment

Dynamic voltage security margin

Real-time security assessment


Hannes Hagmar

Chalmers, Elektroteknik, Elkraftteknik, Elnät och komponenter

Robert Eriksson

Svenska kraftnät

Anh Tuan Le

Chalmers, Elektroteknik, Elkraftteknik, Elnät och komponenter

IET Smart Grid

2515-2947 (eISSN)

Vol. 3 4 470-478

Avancerad visualisering av spänningsstabilitetsgränser och systemskydd baserat på realtidsmätningar

Svenska kraftnät, 2016-06-01 -- 2020-12-31.

Energimyndigheten, 2016-06-01 -- 2020-12-31.


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