access icon openaccess Fast dynamic voltage security margin estimation: concept and development

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 01/10/2019, Accepted 25/03/2020, Revised 12/02/2020, Published 06/04/2020

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 better measure of security than the more commonly used static voltage security margin (VSM). Using the concept of transient P - V curves, this study first establishes and visualises the circumstances when the DVSM is to prefer the static VSM. To overcome the computational difficulties in estimating the DVSM, this study 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 ∼70%, allowing system operators to perform the estimations in near real-time.

Inspec keywords: power system security; power system control; power engineering computing; power system faults; learning (artificial intelligence); neural nets; power system stability

Other keywords: DVSM; time-domain simulations; Nordic32 test system; dynamic system response; static VSM; fast dynamic voltage security margin estimation; static voltage security margin; machine learning-based method; trained neural networks

Subjects: Power system control; Knowledge engineering techniques; Control of electric power systems; Neural computing techniques; Power engineering computing

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