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基于神经网络的独立光伏系统自适应全局滑模 MPPT 控制器设计。

Neural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems.

机构信息

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

Centre for Advanced Studies in Telecommunications (CAST), COMSATS University, Islamabad, Pakistan.

出版信息

PLoS One. 2022 Jan 20;17(1):e0260480. doi: 10.1371/journal.pone.0260480. eCollection 2022.

DOI:10.1371/journal.pone.0260480
PMID:35051183
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8775327/
Abstract

The increasing energy demand and the target to reduce environmental pollution make it essential to use efficient and environment-friendly renewable energy systems. One of these systems is the Photovoltaic (PV) system which generates energy subject to variation in environmental conditions such as temperature and solar radiations. In the presence of these variations, it is necessary to extract the maximum power via the maximum power point tracking (MPPT) controller. This paper presents a nonlinear generalized global sliding mode controller (GGSMC) to harvest maximum power from a PV array using a DC-DC buck-boost converter. A feed-forward neural network (FFNN) is used to provide a reference voltage. A GGSMC is designed to track the FFNN generated reference subject to varying temperature and sunlight. The proposed control strategy, along with a modified sliding mode control, eliminates the reaching phase so that the sliding mode exists throughout the time. The system response observes no chattering and harmonic distortions. Finally, the simulation results using MATLAB/Simulink environment demonstrate the effectiveness, accuracy, and rapid tracking of the proposed control strategy. The results are compared with standard results of the nonlinear backstepping controller under abrupt changes in environmental conditions for further validation.

摘要

日益增长的能源需求和减少环境污染的目标使得使用高效、环保的可再生能源系统变得至关重要。这些系统之一是光伏 (PV) 系统，它根据环境条件（如温度和太阳辐射）的变化产生能量。在存在这些变化的情况下，有必要通过最大功率点跟踪 (MPPT) 控制器来提取最大功率。本文提出了一种基于非线性广义全局滑模控制器 (GGSMC) 的方法，使用 DC-DC 降压-升压转换器从光伏阵列中获取最大功率。采用前馈神经网络 (FFNN) 提供参考电压。设计了一个 GGSCM 来跟踪 FFNN 生成的参考值，以适应不同的温度和阳光。所提出的控制策略与改进的滑模控制相结合，可以消除趋近阶段，从而使滑模在整个时间内存在。系统响应没有抖动和谐波失真。最后，使用 MATLAB/Simulink 环境进行的仿真结果表明，所提出的控制策略具有有效性、准确性和快速跟踪能力。结果与环境条件突然变化下非线性回溯控制器的标准结果进行了比较，以进一步验证。

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