基于人工神经网络的最大功率点跟踪算法

Muhammad Nizar Habibi, Mas Sulung Wisnu Jati, Novie Ayub Windarko, Anang Tjahjono
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引用次数: 3

摘要

利用太阳能电池板可以将太阳能转化为电能,但能量转化率仍然很低。最大功率点跟踪(MPPT)是一种在将电能转换到太阳能电池板的过程中用于增加能量生产的方法。人工神经网络(Artificial Neural Network, ANN)是一种可以应用于MPPT的软计算方法,具有学习过程稳定、速度快、不需要复杂的数学建模、性能好等优点。人工神经网络以太阳能电池板的短路电流为输入,并作为人工神经网络达到最大功率的参考。检测短路电流的过程是由太阳能电池板的功率瞬间下降来表示的。结果表明,该算法在辐射变化的情况下,仍能达到太阳能电池板的最大功率工作点。在最大功率工作点时,人工神经网络可以保持该值,因此结果值不会改变,不会产生纹波。在1000w / m2的辐射和100wp下,人工神经网络可以在0.063秒的时间内产生99.97瓦的最大功率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Maximum Power Point Tracking Menggunakan Algoritma Artificial Neural Network Berbasis Arus Hubung Singkat Panel Surya
The conversion of solar energy into electrical can be utilized by using the solar panel, but the energy conversion ratio is still low. Maximum Power Point Tracking (MPPT) is a method used to increase energy production in the process of converting electrical to the solar panel. Artificial Neural Network (ANN) is one of the soft-computing methods that can be applied as MPPT with the advantage of having a learning process, very stable, fast, doesn’t require complicated mathematical modeling, and has good performance. ANN is proposed with input from the short circuit current of the solar panel and is used as a reference for the ANN to reach the maximum power. The process of detecting a short circuit current is indicated by a momentary decrease of the power by the solar panel. The results show the proposed algorithm can reach the maximum power operating point of the solar panel despite the change of radiation. When at maximum power operating point, ANN can hold the value, so the resulting value doesn’t change and doesn’t generate ripple. At radiation of 1000 W/m 2 and using 100 WP, ANN can produce a maximum power of 99.97 Watts with a time of 0.063 seconds.
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