| 引用本文: | 赵文清,李瑶.利用智能水滴算法优化神经网络的MPPT[J].电力自动化设备,2017,37(7): |
| ZHAO Wenqing,LI Yao.MPPT based on neural network optimized by intelligent water drop algorithm[J].Electric Power Automation Equipment,2017,37(7): |
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| 摘要: |
| 光伏系统最大功率点跟踪(MPPT)对于提高光伏发电效率有着重大意义。给出一种智能水滴(IWD)算法优化Elman神经网络的MPPT方法。利用IWD算法对Elman神经网络的权值和阈值进行优化，提高Elman神经网络的训练效果。将IWD算法优化Elman神经网络的MPPT方法与传统预测方法进行对比，结果验证了所提方法的有效性。 |
| 关键词: 光伏系统 MPPT 智能水滴算法 Elman神经网络 |
| DOI：10.16081/j.issn.1006-6047.2017.07.002 |
| 分类号:TM615 |
| 基金项目:中央高校基本科研业务费专项资金资助项目(12MS121) |
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| MPPT based on neural network optimized by intelligent water drop algorithm |
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ZHAO Wenqing, LI Yao
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School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
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| Abstract: |
| MPPT(Maximum Power Point Tracking) of photovoltaic system is of great significance for improving the efficiency of photovoltaic power generation. A method of MPPT based on Elman neural network optimized by IWD(Intelligent Water Drop) algorithm is proposed, which applies IWD algorithm to optimize the weights and thresholds of Elman neural network for improving its training efficiency. The proposed method is compared with traditional prediction methods to verify its effectiveness. |
| Key words: photovoltaic system MPPT intelligent water drop algorithm Elman neural network |
