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基于大数据分析的输变电设备状态数据异常检测方法

DOI: 10.13334/j.0258-8013.pcsee.2015.01.007, PP. 52-59

Keywords: 大数据,异常检测,时间序列,神经网络,无监督聚类

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Abstract:

传统的阈值判定方法难以准确检测输变电设备的状态异常,该文提出一种基于时间序列分析和无监督学习等大数据分析的异常检测方法,从数据演化过程、数据关联的全新角度实现异常检测。通过时间序列模型和自适应神经网络对历史数据潜在的特征进行挖掘,并将数据对时间的动态变化规律用转移概率序列表示。针对多维的监测数据,运用无监督聚类方法简化各参量之间的相关关系,从而避免参量间相关性难以确定的问题。提出异常检测体系,并使之适用于输变电设备状态监测数据流,实现数据流中异常的快速检出。最后结合运行实例验证了提出方法的有效性,表明本方法能快速检测出设备的异常运行状态。

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