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基于非线性自适应神经模糊反馈线性化的微电网光伏系统最大功率点跟踪控制方案。

Nonlinear adaptive NeuroFuzzy feedback linearization based MPPT control schemes for photovoltaic system in microgrid.

机构信息

Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad, Pakistan.

出版信息

PLoS One. 2020 Jun 30;15(6):e0234992. doi: 10.1371/journal.pone.0234992. eCollection 2020.

DOI:10.1371/journal.pone.0234992
PMID:32603382
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7326197/
Abstract

Renewable energy resources connected to a single utility grid system require highly nonlinear control algorithms to maintain efficient operation concerning power output and stability under varying operating conditions. This research work presents a comparative analysis of different adaptive Feedback Linearization (FBL) embedded Full Recurrent Adaptive NeuroFuzzy (FRANF) control schemes for maximum power point tracking (MPPT) of PV subsystem tied to a smart microgrid hybrid power system (SMG-HPS). The proposed schemes are differentiated based on structure and mathematical functions used in FRANF embedded in the FBL model. The comparative analysis is carried out based on efficiency and performance indexes obtained using the power error between the reference and the tracked power for three cases; a) step change in solar irradiation and temperature, b) partial shading condition (PSC), and c) daily field data. The proposed schemes offer enhanced convergence compared to existing techniques in terms of complexity and stability. The overall performance of all the proposed schemes is evaluated by a spider chart of multivariate comparable parameters. Adaptive PID is used for the comparison of results produced by proposed control schemes. The performance of Mexican hat wavelet-based FRANF embedded FBL is superior to the other proposed schemes as well as to aPID based MPPT scheme. However, all proposed schemes produce better results as compared to conventional MPPT control in all cases. Matlab/Simulink is used to carry out the simulations.

摘要

可再生能源资源连接到单个公用事业电网系统需要高度非线性控制算法，以在不同的运行条件下保持高效的功率输出和稳定性。本研究工作对不同的自适应反馈线性化（FBL）嵌入式全递归自适应神经模糊（FRANF）控制方案进行了比较分析，用于与智能微电网混合电源系统（SMG-HPS）连接的光伏子系统的最大功率点跟踪（MPPT）。所提出的方案基于在 FBL 模型中嵌入的 FRANF 中使用的结构和数学函数进行区分。基于三种情况下参考功率和跟踪功率之间的功率误差获得的效率和性能指标进行比较分析：a）太阳辐照度和温度的阶跃变化，b）部分阴影条件（PSC），和 c）日常现场数据。与现有技术相比，所提出的方案在复杂性和稳定性方面提供了更好的收敛性。通过多元可比参数的蜘蛛图评估所有提出方案的整体性能。自适应 PID 用于比较所提出的控制方案产生的结果。基于墨西哥帽小波的 FRANF 嵌入式 FBL 的性能优于其他提出的方案以及基于 PID 的 MPPT 方案。然而，与所有情况下的常规 MPPT 控制相比，所有提出的方案在所有情况下都能产生更好的结果。使用 Matlab/Simulink 进行仿真。

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