电力系统保护与控制2025，Vol.53Issue(2)：62-72,11.DOI:10.19783/j.cnki.pspc.240749
基于PSO-ELM的可植入UPQC的"源-网-荷-储"系统最优控制策略
Optimum control for UPQC-embedded source-network-load-storage system using PSO-ELM
摘要
Abstract
To address the issues of low renewable energy penetration and poor power quality in traditional source-network-load-storage(SNLS)systems,an optimum control scheme for an SNLS system embedded with a unified power quality conditioner(UPQC)is presented.The proposed scheme is implemented using the particle swarm optimization(PSO)based extreme learning machine(ELM)algorithm.In the multi-objective optimization operation scheme:the first optimization objective is to maximize the power generation of photovoltaic(PV)arrays;the second and third optimization objectives are to minimize the load voltage deviation and maximize the network side power factor,respectively;and the fourth optimization objective is to maximize the utilization rate of the converter.Since the multi-objective optimization problems are difficult to solve in real-time,a hierarchical optimization approach based on the priority order of optimization objectives is presented to simplify the multi-objective optimization problem into several single-objective ones.Then,by training all the optimum solution sets obtained as an PSO-ELM surrogate model,the proposed strategy can be executed quickly and accurately.Finally,the effectiveness of the proposed scheme is verified through simulations.The case studies show that the proposed strategy can improve the absorption rate of renewable energy and the utilization rate of converters,and optimize power quality.关键词
统一电能质量调节器/"源-网-荷-储"系统/光伏/PSO-ELMKey words
unified power quality conditioner(UPQC)/source-network-load-storage(SNLS)/photovoltaic(PV)/particle swarm optimization-based extreme learning machine(PSO-ELM)引用本文复制引用
高波,刘川,韩建,李泽文,韦宝泉..基于PSO-ELM的可植入UPQC的"源-网-荷-储"系统最优控制策略[J].电力系统保护与控制,2025,53(2):62-72,11.基金项目
This work is supported by National Natural Science Foundation of China(No.52407101,No.52467006 and No.52367015). 国家自然科学基金项目资助(52407101,52467006,52367015) （No.52407101,No.52467006 and No.52367015）
江西省自然科学基金项目资助(20232BAB214061,2023BAB214065) （20232BAB214061,2023BAB214065）
