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并网混合可再生能源系统中光伏与固体氧化物燃料电池的自适应控制范式

Adaptive control paradigm for photovoltaic and solid oxide fuel cell in a grid-integrated hybrid renewable energy system.

作者信息

Mumtaz Sidra, Khan Laiq

机构信息

Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad, Khyber Pakhtunkhwa, Pakistan.

出版信息

PLoS One. 2017 Mar 22;12(3):e0173966. doi: 10.1371/journal.pone.0173966. eCollection 2017.

DOI:10.1371/journal.pone.0173966
PMID:28329015
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5362096/
Abstract

The hybrid power system (HPS) is an emerging power generation scheme due to the plentiful availability of renewable energy sources. Renewable energy sources are characterized as highly intermittent in nature due to meteorological conditions, while the domestic load also behaves in a quite uncertain manner. In this scenario, to maintain the balance between generation and load, the development of an intelligent and adaptive control algorithm has preoccupied power engineers and researchers. This paper proposes a Hermite wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control of photovoltaic (PV) systems to extract maximum power and a Hermite wavelet incorporated NeuroFuzzy indirect adaptive control of Solid Oxide Fuel Cells (SOFC) to obtain a swift response in a grid-connected hybrid power system. A comprehensive simulation testbed for a grid-connected hybrid power system (wind turbine, PV cells, SOFC, electrolyzer, battery storage system, supercapacitor (SC), micro-turbine (MT) and domestic load) is developed in Matlab/Simulink. The robustness and superiority of the proposed indirect adaptive control paradigm are evaluated through simulation results in a grid-connected hybrid power system testbed by comparison with a conventional PI (proportional and integral) control system. The simulation results verify the effectiveness of the proposed control paradigm.

摘要

由于可再生能源的丰富可用性，混合动力系统（HPS）是一种新兴的发电方案。可再生能源由于气象条件的原因，其本质上具有高度间歇性，而家庭负载的行为方式也相当不确定。在这种情况下，为了维持发电与负载之间的平衡，智能且自适应控制算法的开发一直是电力工程师和研究人员关注的重点。本文提出了一种用于光伏（PV）系统的嵌入埃尔米特小波的神经模糊间接自适应最大功率点跟踪（MPPT）控制方法，以提取最大功率；以及一种用于固体氧化物燃料电池（SOFC）的并入埃尔米特小波的神经模糊间接自适应控制方法，以便在并网混合动力系统中获得快速响应。在Matlab/Simulink中开发了一个用于并网混合动力系统（风力涡轮机、光伏电池、SOFC、电解槽、电池存储系统、超级电容器（SC）、微型涡轮机（MT）和家庭负载）的综合仿真试验平台。通过在并网混合动力系统试验平台中的仿真结果，与传统比例积分（PI）控制系统进行比较，评估了所提出的间接自适应控制范式的鲁棒性和优越性。仿真结果验证了所提出控制范式的有效性。

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