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首页> 外文会议>Proceedings of the 6th European Embedded Design in Education and Research Conference >Microcontroller based maximum power point tracking through FCC and MLP Neural Networks
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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：本文涵盖了对实现基于神经网络的光伏系统效率控制方法的研究。该算法旨在在气候条件突然发生变化时跟踪PV设备的最大功率点。该算法的核心是通过使用光伏设备的合适数学模型训练的神经网络（NN）。研究了不同的NN体系结构和几种优化解决方案，以研究在计算效率，内存占用量和预测准确性方面的最佳方法。通过使用原型板来驱动低功耗太阳能电池的工作点，德州仪器（Texas Instruments）在微控制器单元LM4F120H5QR上实现并测试了发现最好的架构。

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