Fast Dynamic Voltage Security Margin Estimation: Concept and Development
Review article, 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

Author

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 4 470-478

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

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

Swedish Energy Agency, 2016-06-01 -- 2020-12-31.

Subject Categories

Other Computer and Information Science

Computer Science

Computer Systems

Driving Forces

Sustainable development

Areas of Advance

Energy

DOI

10.1049/iet-stg.2019.0278

More information

Latest update

5/7/2021 1