Work

Optimum control for UPQC-embedded source-network-load-storage system using extreme learning machine scheme

Year: 2025

Type: article

Abstract: This study presents an optimum control scheme for the unified power quality conditioner (UPQC)-embedded source-network-load-storage system (SNLSS) to improve the penetration of renewable energy, power quality (PQ) and system utilization rate simultaneously. In the proposed optimum control scheme, four optimization objectives are considered and they include: 1) maximizing the output power of PV arrays to improve the related penetration rate; 2) minimizing the load voltage deviation to improve the voltage quality; 3) maximizing the power factor of the power grid the improve the current quality; and 4) maximizing the utilization rate of proposed system to make full use of the UPQC. By adopting the hierarchical optimization idea, the multi-objective optimization problem is converted into a few single-objective ones. Then, all optimum solutions are solved and trained as an ELM surrogate model, which realizes the quick and precise implementation of optimum control. Time-domain simulation results demonstrate the usefulness of the proposed optimum control scheme.

Source: International Journal of Electrical Power & Energy Systems

DOI: 10.1016/j.ijepes.2025.111462

Authors: Bo Gao, Yuchen Zhang, Jian Han, Zewen Li, Fangming Deng

Institution: East China Jiaotong University

Language: English

Topic: Power Quality and Harmonics

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,920