Fast dynamic voltage security marginestimation: concept and development
Journal article, 2020

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 bettermeasure of security than the more commonly used static voltage security margin (VSM). Using the concept of transient P - Vcurves, this study first establishes and visualises the circumstances when the DVSM is to prefer the static VSM. To overcomethe computational difficulties in estimating the DVSM, this study proposes a method based on training two separate neuralnetworks on a data set composed of combinations of different operating conditions and contingency scenarios generated usingtime-domain simulations. The trained neural networks are used to improve the search algorithm and significantly increase thecomputational efficiency in estimating the DVSM. The machine learning-based approach is thus applied to support theestimation of the DVSM, while the actual margin is validated using time-domain simulations. The proposed method was testedon the Nordic32 test system and the number of time-domain simulations was possible to reduce with ∼70%, allowing systemoperators to perform the estimations in near real-time.


Hannes Hagmar

Chalmers, Electrical Engineering, Electric Power Engineering, Power grids and Components

Robert Eriksson

Swedish national grid

Anh Tuan Le

Chalmers, Electrical Engineering, Electric Power Engineering, Power grids and Components

IET Smart Grid

2515-2947 (eISSN)

Vol. 3 4 370-378

Advanced visualization of voltage stability limit and system protection based on real-time measurement

Swedish Energy Agency (44358-1), 2016-06-01 -- 2020-12-31.

Swedish national grid, 2016-06-01 -- 2020-12-31.

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Sustainable development

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Computer Science

Computer Systems

Other Electrical Engineering, Electronic Engineering, Information Engineering



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