基于离散傅里叶分解的电能质量不平衡度指标AR预测方法  

AR Prediction Method of Unbalance Factor Index of Power Quality Based on Discrete Fourier Decomposition

作　　者：崔晓飞 马智远 许中 温从溪[2] 

机构地区：[1]广州供电局有限公司电力试验研究院,广东广州510410 [2]华北电力大学电气与电子工程学院,北京102206

出　　处：《现代电力》2013年第6期38-42,共5页Modern Electric Power

摘　　要：提出了一种基于离散傅里叶分析的时间序列自回归方法(AR),用于电能质量不平衡度指标的预测。首先利用离散傅里叶变换对指标序列进行频域分析,提取指标序列的低频分量,对指标序列的各个低频分量分别利用时间序列自回归方法进行预测,然后对这些分量预测值进行离散傅里叶反变换,得到不平衡度指标序列的预测值。通过实际数据验证,离散傅里叶分析能够减小高频噪声的影响,对比BP神经网络预测方法,结果表明,所提出的基于离散傅里叶变换的AR方法可以对不平衡度指标进行有效预测。An autoregressive （AR） method based on discrete Fourier analysis is proposed to forecast unbalance factor index of power quality.The discrete Fourier transform of the sequence of unbalance factor index is analyzed,and the low frequency components are extracted.An autoregressive method is used to forecast each low frequency component of index sequence.Through inverse discrete Fourier transform,the forecasting low frequency components are converted to forecasting sequence of the unbalance factor index in time domain.The proposed method is verified by actual data,and the results show that discrete Fourier analysis can reduce the influence of high frequency noise.By comparing with BP neural network prediction method,the AR method based on Fourier transform can more effectively forecast unbalance factor index.

关 键 词：电能质量 不平衡度指标预测 离散傅里叶 AR模型 BP神经网络 

分 类 号：TM744[电气工程—电力系统及自动化]