Distributed PV Identification Based on High-Precision Bus Data Analysis

Xincheng Shen, Shaoxiong Huang, Zhi Li, Kaifeng Zhang
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Abstract

With the rapid development of distributed photovoltaic (PV), it is necessary to study its low-cost output identification technology. In this paper, a low-cost PV output identification method is proposed by using feature extraction. This paper analyzes the high-precision bus data, and uses harmonic analysis, wavelet analysis and Ensemble Empirical Mode Decomposition (EEMD) to extract the operating features of PV output. Then this paper screens these extracted features with the correlation between features and PV output, the stability of the features at different times and the difference of features in different signals. The appropriate features are selected for PV output identification, and its identification accuracy is calculated. The experimental results show that with the method of the Ensemble Empirical Mode Decomposition, an appropriate operating feature can be extracted. This feature can identify the distributed PV output in small bus bar when the PV is working stably.
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基于高精度总线数据分析的分布式光伏识别
随着分布式光伏的快速发展,有必要对其低成本输出识别技术进行研究。本文提出了一种基于特征提取的低成本光伏输出识别方法。本文对高精度总线数据进行分析,利用谐波分析、小波分析和集成经验模态分解(EEMD)提取光伏输出的运行特征。然后根据特征与PV输出的相关性、特征在不同时刻的稳定性以及特征在不同信号中的差异性对提取的特征进行筛选。选择合适的特征进行光伏输出识别,并计算其识别精度。实验结果表明,采用集成经验模态分解方法可以提取出合适的操作特征。该特性可以在PV稳定工作时识别分布式PV在小母线上的输出。
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