基于软计算神经网络的太阳能光伏发电MPPT技术性能分析

Sunita Chahar, D. K. Yadav
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引用次数: 4

摘要

本文提出了一种利用基于软计算人工神经网络(ANN)的最大功率点跟踪(MPPT)方案,使太阳能光伏发电机组高效工作并产生最大功率的解决方案。采用一种基于软计算的人工神经网络技术,在给定的动态辐照条件下,对自识别的最大功率进行计算。从计算历元的角度和均方误差的角度比较了所提出的方法在给定数据模式和生成数据模式下的性能。在自生成数据模式的情况下,报告的模拟结果发现,所提出的太阳能光伏发电机配置技术具有优越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Analysis of Soft Computing ANN Based MPPT Technique for Solar PV Generator
This paper proposes the solution to finalize a way to use the soft computing artificial neural network (ANN) based MPPT (maximum power point tracking) scheme for a solar photovoltaic (PV) generator to work efficiently and produce maximum power. A better state-of-the-art technique based on soft computing ANN to take out maximum power for self-identified and given dynamic irradiation conditions is used. The comparison of the performance of the proposed methodology for given data pattern and generated data pattern is presented in terms of computational epoch point of view and mean square error. The reported simulation results in the case of self-generated data patterns found superior performance for the proposed technique for Solar PV generator configuration.
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