基于高斯混合模型的光伏发电出力中高比例异常数据检测方法研究

光伏发电出力中的高比例异常数据具有相互重叠、多高斯分布的特点,如何精确地剔除光伏发电出力中的高比例异常数据是实现光伏功率精确预测的关键问题.文章分析了光伏发电出力中高比例异常数据的特点,并研究了一种基于高斯混合模型的光伏发电出力高比例异常数据检测方法.建立了高斯混合模型的算法模型,使用期望极大算法(Expectation Maximization,EM)对高斯混合模型的参数进行估计,使用算法模型对光伏发电出力中的高比例异常数据进行检测和剔除.实际算例分析和对比实验表明,文章的方法可以对多分类的高比例异常数据进行精确地检测,较传统的异常数据检测方法更加适用于光伏发电出力的高比例异常数据检测.

Research of High Proportion of Outliers Detection in Photovoltaic Power Data based on Gaussian Mixture Model

The high proportion of outliers in photovoltaic power data has the characteristics of mutual overlap and multi-Gaussian distribution. How to accurately eliminate the high proportion of outliers from photovoltaic power data is a crucial problem for realizing precise photovoltaic power forecast. This paper analyzes the characteristics of high proportion of outliers in photovoltaic power data, and studies a high proportion of outlier detection method in photovoltaic power data based on Gaussian mixture model. Firstly, the algorithm model of Gaussian mixture model is established, and then the EM algorithm is used to estimate parameters of the Gaussian mixture model. At last, the proposed algorithm is used to detect and eliminate the high proportion of outliers in the photovoltaic power data. Experiments and comparative studies show that the proposed method is more suitable for high proportion of outlier detection in photovoltaic power than the classical outlier data detection method.