Fast dynamic voltage security margin estimation: concept and development. Issue 4 (29th May 2020)

• Record Type: Journal Article
• Authors: Hagmar, Hannes; Eriksson, Robert; Tuan, Le Anh
• Is Part Of: IET smart grid. Volume 3:Issue 4(2020)
• Page Start: 470
• Page End: 478
• Publication Date: 2020-05-29
• Subjects: power system control -- neural nets -- learning (artificial intelligence) -- power system security -- power system faults -- power system stability -- power engineering computing; time‐domain simulations -- fast dynamic voltage security margin estimation -- machine learning‐based method -- DVSM -- dynamic system response -- static VSM -- trained neural networks -- static voltage security margin -- Nordic32 test system; B8110C Power system control -- C3340H Control of electric power systems -- C5290 Neural computing techniques -- C6170K Knowledge engineering techniques -- C7410B Power engineering computing; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2019.0278
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : 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.