| Title |
A Study on Voltage Control in Distribution Systems Through Substation AVR Adjustment Based on Voltage Estimation Techniques |
| Authors |
임병창(Byeongchang Lim) ; 유연태(Yeuntae Yoo) |
| DOI |
https://doi.org/10.5370/KIEE.2026.75.4.724 |
| Keywords |
Distribution System; Line Drop Compensation(LDC); On-Load Tap Changer(OLTC); Machine Learning; Voltage Control |
| Abstract |
As the integration of Distributed Energy Resources (DERs) into distribution systems increases, challenges such as voltage instability due to reverse power flow and frequent tap operations of On-Load Tap Changers (OLTCs) are intensifying. Conventional Line Drop Compensation (LDC) methods fail to accurately predict the load center voltage under reverse power flow conditions, leading to unnecessary tap adjustments, which directly cause a reduction in the mechanical lifespan of OLTCs and an increase in maintenance costs. To overcome these limitations, this study proposes a new machine learning-based LDC technique that introduces a dynamic ‘correction factor ’ predicted by machine learning, while maintaining the structural simplicity of the conventional LDC formula. The proposed technique significantly improves voltage estimation accuracy by feeding real-time data available at the substation into a LightGBM model to predict the optimal correction factor , which reflects the ever-changing system conditions. To verify the performance of the proposed technique, a two-year time-series simulation was conducted on a modified IEEE 34-bus system built with OpenDSS, using actual weather and load data. The results showed that the proposed technique reduced the total number of OLTC tap operations by up to 25.9% compared to the conventional method, and the voltage stability range maintenance ratio significantly improved from 28.7% to 96.0% in the most unstable phase. This demonstrates that the proposed technique is a practical and effective solution that can extend the lifespan of OLTCs and simultaneously enhance the power quality and DER hosting capacity of distribution systems by intelligently upgrading the operation of existing equipment. |