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Microcontroller based maximum power point tracking through FCC and MLP Neural Networks

机译：通过FCC和MLP神经网络的基于微控制器的最大功率点跟踪

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This paper covers the study towards the implementation of a Neural Network based approach for the efficiency control of Photovoltaic systems. The algorithm aims to track the maximum power point for the PV device whenever abrupt changes in climatic conditions occur. The core of the algorithm is a Neural Network (NN) trained by using a suitable mathematical model of the photovoltaic device. Different NN architectures and several optimization solutions were studied to investigate the best approach in terms of computational efficiency, memory footprint and prediction accuracy. The best found architecture has been implemented and tested on the microcontroller unit LM4F120H5QR by Texas Instruments by using a prototype board to drive the operating point of a low-power solar cell.
机译：本文涵盖了实现神经网络基于效率控制的基于神经网络的光伏系统的研究。 该算法旨在在发生气候条件的突然变化时跟踪PV器件的最大功率点。 该算法的核心是通过使用光伏器件的合适数学模型训练的神经网络（NN）。 研究了不同的NN架构和几种优化解决方案，以研究计算效率，内存足迹和预测准确性的最佳方法。 通过使用原型板通过德州仪器在微控制器单元LM4F120H5QR上实现和测试了最佳发现的架构，以驱动低功率太阳能电池的操作点。

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