王育飞;付玉超;孙路;薛花;

• 1:上海电力学院电气工程学院
• 2:国网上海市电力公司

摘要(Abstract)：

光伏出力时间序列的随机性和波动性使得光伏出力预测难以达到理想的精度,而提高预测精度是降低光伏并网不利影响的前提。因此,在揭示光伏出力波动本质的基础上,提出了混沌-径向基函数(radial basis function,RBF)预测模型。首先,应用小波去噪处理后光伏发电功率实测数据,基于C-C法重构系统相空间,运用相图法和改进最大李雅普诺夫(Lyapunov)指数法,对输出功率进行非线性动力学分析,确定光伏出力具有混沌特性;然后,分析光伏出力相空间轨迹局部变化的规律性,采用RBF神经网络对系统轨迹进行拟合,建立基于混沌-RBF神经网络的光伏发电功率超短期预测模型;最后,选取去噪后的实测数据对所建模型进行训练和预测,验证模型在不同天气状况下的预测效果。结果表明,所建模型对晴朗、多云和阴天等天气状况都有较好的预测精度,显示出良好的预测性能。

关键词(KeyWords)： 光伏发电;功率波动;混沌特性;RBF神经网络;超短期预测

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(51407114);; 上海市自然科学基金项目(15ZR1418000)~~

作者(Author): 王育飞;付玉超;孙路;薛花;

Email:

DOI: 10.13335/j.1000-3673.pst.2017.2878

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