Fast dynamic voltage security margin estimation: concept and development

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.

Keywords

• 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