RT journal article
T1 Optimal Control for On-Load Tap-Changers and Inverters in Photovoltaic Plants Applying Teaching Learning Based Optimization
A1 Silva Quiñones, Rolando A.
A1 Sánchez Sainz, Higinio
A1 García Triviño, Pablo
A1 Sarrias Mena, Raúl
A1 Carrasco González, David
A1 Fernández Ramírez, Luis Miguel
A2 Ingeniería Eléctrica
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 Photovoltaic plants
K1 On Load Tap Changer (OLTC) transformer
K1 Energy Management System (EMS)
K1 Teaching Learning Based Optimization (TLBO)
AB This research presents an optimized control strategy for the coordinated operation of parallel grid connected photovoltaic (PV) plants and an On Load Tap Changer (OLTC) transformer. The proposed framework integrates inverter-level active and reactive power dispatch with OLTC tap control through an Energy Management System (EMS) based on an improved Teaching Learning Based Optimization (TLBO) algorithm. The EMS minimizes operational costs while maintaining voltage stability and respecting electrical and mechanical constraints. Comparative analyses with Monte Carlo, fmincon, and conventional TLBO methods demonstrate that the optimized TLBO achieves up to two orders of magnitude faster convergence and higher robustness, enabling more reliable performance under variable irradiance and load conditions. Simulation and Hardware-in-the-Loop (HIL) results confirm that the coordinated OLTC inverter control significantly enhances reactive power capability and voltage regulation. The proposed optimized TLBO based EMS offers an effective and computationally efficient solution for dynamic energy management in medium scale PV systems, supporting grid reliability and maximizing renewable energy utilization.
PB MDPI
SN 2079-9292
YR 2025
FD 2025-10
LK http://hdl.handle.net/10498/38358
UL http://hdl.handle.net/10498/38358
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026