Savasci, A.Ceylan, O.Paudyal, S.2025-04-152025-04-1520249798350379730https://doi.org/10.1109/UPEC61344.2024.10892461https://hdl.handle.net/20.500.12469/7280This study presents an off-line optimization-guided machine learning approach for coordinating the local control rules of on-load tap changers (OLTCs) and step-voltage regula-tors (SVRs). Based on a bang-bang control rule, these legacy devices autonomously regulate the feeder voltage around the nominal level by varying the tap position in the lower or raise direction. The characterizing parameter of the local control rule is the dead band, which affects the number of tap switching in operation and is directly related to the economical use life of the equipment. The bandwidth is typically set within a standard voltage range and is generally kept constant in daily operation. However, adjusting the bandwidth dynamically can prevent excessive tap switching while maintaining satisfactory voltage regulation for varying loading and distributed generation conditions. Our approach aims to set the bandwidth parameter systematically and efficiently through a machine learning-based scheme, which is trained with a dataset formed by solving the distribution network optimal power flow (DOPF) problem. The performance of learning the bandwidth parameter is demonstrated on the modified 33-node feeder, which is promising for integrated voltage control schemes. © 2024 IEEE.eninfo:eu-repo/semantics/closedAccessDistribution GridMachine LearningOptimal Power FlowVoltage ControlConference Object10.1109/UPEC61344.2024.108924612-s2.0-86000797359N/AN/A