考虑数据相关性的光伏电站不良数据识别与重构

Identification and reconstruction of bad data in photovoltaic power stations considering autocorrelation of multi-source heterogeneous data

中文关键词: 光伏系统  多源异构数据  不良数据识别  数据重构  多源参数相关性

英文关键词: photovoltaic systems  multi-source heterogeneous data  identification of bad data  data reconstruction  correlation of multiple Parameters

中文摘要:

      随着光伏发电渗透率的不断提高,数据质量问题已成为影响光伏电站智能化运维和并网研究的关键因素。不良数据的存在不仅会影响预测的准确性,还可能导致光伏系统状态监测和故障诊断的偏差。为提高光伏电站数据完整性和可靠性,提出一种基于多源异构数据相关性的光伏电站不良数据识别与重构方法。首先,分析光伏系统正常运行情况下的数据特征及多源参数间的相关性,筛选与待重构日数据特征最为相似的历史数据作为输入；其次,基于相对密度的多密度聚类算法对功率不良数据进行识别清洗；最后,结合环境数据相关性建立光伏系统组合长短期记忆数据重构模型,实现对数据的高精度重构。算例结果表明,所提方法可以有效地识别光伏电站出力的不良数据并准确重构。

英文摘要:

      With the continuous increase in the penetration rate of photovoltaic power generation, data quality have become a key factor affecting the intelligent operation and grid connection research of photovoltaic power plants. The existence of bad data not only affects the accuracy of predictions, but may also lead to deviations in photovoltaic system status monitoring and fault diagnosis. To improve the integrity and reliability of photovoltaic power plant data, this paper proposes a method for identifying and reconstructing bad data in photovoltaic power plants based on multi-source heterogeneous data correlation. Firstly, analyze the data characteristics and correlation between multisource parameters of the photovoltaic system under normal operation, and select the historical data with the most similar characteristics to the data to be reconstructed as input. Secondly, the multi density clustering algorithm based on relative density is used to identify and clean power poor data. Finally, based on the correlation of environmental data, a photovoltaic system combination long short-term memory data reconstruction model is established to achieve high-precision reconstruction of the data. The calculation results show that the proposed method can effectively identify the bad data of photovoltaic power station output and accurately reconstruct it.