Online Learning-Based ANN Controller for a Grid-Interactive Solar PV System
Online Learning-Based ANN Controller for a Grid-Interactive Solar PV System
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用于电网交互式太阳能光伏系统的基于在线学习的 ANN 控制器
DOI：
10.3390/app11188712
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
T. Senjyu
中科院分区：
文献类型：
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作者：
M. M. Irfan;S. Malaji;Chandarashekhar Patsa;S. Rangarajan;Randolph E. Collins;T. Senjyu
工业4.0的技术转型包括计算机、变速设备等功率转换器和微处理器，这些都分散了对电能质量的关注。分布式发电技术，如太阳能光伏（PV）和风力发电系统与源电网的集成，经常使用电力转换器，这增加了电能质量的问题。DSTATCOM是FACTS设备中最擅长补偿电流相关的电能质量问题。提出了一种基于神经网络控制器的DSTATCOM模型，并采用反向传播在线学习算法实现了该模型。该算法最大限度地减少了数学负担和控制的复杂性。它在改善电网的电力质量方面发挥了积极作用。该算法在MATLAB中实现，使用人工神经网络模型控制器，并使用FPGA控制器的实验装置的结果进行了验证。
The technology transformation of industry 4.0 comprises computers, power converters such as variable speed devices, and microprocessors, which distract from the quality of power. The integration of distribution-generation technologies, such as solar photovoltaic (PV) and wind systems with source grids, frequently uses power converters, which increases the issues with power quality. DSTATCOM is the FACTS device most proficient in recompensing current-related power quality concerns. A model of DSTATCOM with an ANN controller was developed and implemented using a backpropagation online learning-based algorithm for balanced non-linear loads. This algorithm minimized the mathematical burden and the complications of control. It demonstrated a dynamic role in improving the quality of the power at the grid. The algorithm was implemented in MATLAB using an ANN model controller and the results were validated with an experimental set-up using an FPGA controller.