考虑母线电压调节的分布式双投影层递归神经网络多母线直流微电网优化控制。

IF 6.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yuanyuan Zhu , Fan Yang , Guoyu Lin
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引用次数: 0

摘要

随着即插即用负载的广泛应用,对直流微电网的传统控制问题提出了新的挑战。本文研究了考虑母线电压调节的发电成本和输电线路功率损耗联合优化问题。具体来说,首先引入了虚拟负载节点的概念,以便负载可以以即插即用的方式实现。通过Kron Reduction,负载节点母线电压由DG节点间接控制。然后,提出了一种分布式双投影层递归神经网络(DRNN)进行实时最优控制。耦合电压和电流同时保持在安全范围内。通过Lyapunov综合,证明了DRNN的收敛性。通过即插即用试验和对比分析,对所提方法的有效性进行了仿真评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal control of multi-bus DC microgrids based on distributed dual-projection-layer recurrent neural network considering bus voltage regulation
With the broad application of plug-and-play loads, it brings new challenges to conventional control issues in DC microgrids. This work addresses the joint optimization of generation costs and transmission line power losses considering bus voltage regulation. Specifically, the concept of virtual load nodes is first introduced so that loads can be implemented as plug-and-play. Through Kron Reduction, bus voltage of load nodes is indirectly controlled by DG nodes. Then, a distributed dual-projection-layer recurrent neural network (DRNN) is proposed for real-time optimal control. The coupled voltage and current are simultaneously maintained within safe bounds. By using Lyapunov synthesis, the convergence of the DRNN is demonstrated. The effectiveness of the proposed methods is evaluated by simulations in terms of plug-and-play test and comparative analysis.
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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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