摘要
新能源发电曲线的准确预报对电网运行至关重要。针对这类随机波动型时间序列的建模分析,本文研究了3种具有代表性的多尺度分解算法:小波变换、经验模态分解和数学形态学分解。以波动规律复杂的风电时间序列为例,对比了3种分解算法的效果。基于信息熵理论和希尔伯特变换技术,从信息复杂性、幅值特性和频率特性3个方面,建立了一套评价分解算法应用性能的指标体系,并采用分解算法和神经网络的组合预测模型进行验证。结果表明,本文所提评价体系有效,为新能源建模中分解算法的选择和应用提供参考。
Renewable energy generation forecast is important for power grid operations. As for modeling this random and volatility time series,this paper studies three representative multi-scale decomposition algorithms: wavelet transform,empirical mode decomposition,and mathematical morphology. Taking the wind power time series as an example,their decomposition performances are evaluated.Based on information entropy theory and Hilbert transform,an evaluating indices system for algorithm performance is constructed from three aspects,the complexity,amplitude characteristics and frequency characteristics of decomposition results. The proposed evaluating method is verified by the forecast models based on artificial neural network and decomposition algorithms,and the results show that the proposed evaluation system is effective,and provides guidelines for selecting decomposition algorithms.
作者
管霖
卓映君
周保荣
詹勋淞
赵文猛
陈恒安
GUAN Lin;ZHUO Yingjun;ZHOU Baorong;ZHAN Xunsong;ZHAO Wenmeng;CHEN Heng'an(School of Electric Power,South China University of Technology,Guangzhou 510640,China;Electric Power Research Institute,CSG,Guangzhou 510663,China)
出处
《南方电网技术》
CSCD
北大核心
2020年第6期11-16,32,共7页
Southern Power System Technology
基金
广东省重点领域研发计划(2019B111109001)
国家自然科学基金国际(地区)合作与交流项目(51761145106)
南方电网公司重点科技项目(CSGTRC-K163007)。
关键词
时间序列预测
多尺度分解算法
风电
信息熵
可再生能源
time series forecast
multi-scale decomposition algorithm
wind power
information entropy
renewable energy
