基于数据挖掘的分布式光伏发电量异常检测方法

Anomaly Detection Method of Distributed Photovoltaic Power Generation Based on Data Mining

为扩大光伏发电量异常检测范围,保证发电过程的安全性与稳定性,针对基于数据挖掘的分布式光伏发电量异常检测方法展开研究。通过分布式光伏发电监测与发电量信息获取、基于数据挖掘的分布式光伏发电类型划分、基于离散点分析的发电量异常检测算法设计,完成检测方法的设计。通过对比实验证明,相比传统方法,设计的检测方法不仅可以实现对发电量异常的精准检测,同时也可以提高传统检测方法的检测范围。

In order to expand the range of abnormal detection of photovoltaic power generation and ensure the safety and stability of the power generation process,the distributed photovoltaic power generation abnormal detection method based on data mining is studied.Through distributed photovoltaic power generation monitoring and power generation information acquisition,distributed photovoltaic power generation type classification based on data mining,and abnormal power generation detection algorithm design based on discrete point analysis,the detection method is designed.Through comparative experiments,it is proved that compared with traditional methods,the designed detection method can not only achieve accurate detection of abnormal power generation,but also improve the detection range of traditional detection methods.