分布式并行FP-growth算法在二次设备缺陷监测中的应用

Application of a distributed parallel FP-growth algorithm in secondary device defects monitoring

智能变电站设备监控数据存储分散,主站获取设备缺陷特征的难度大,有必要通过分布式数据挖掘的方法发现设备缺陷和信号之间的关系。分布式并行频繁模式树(FP-growth)算法采用Hadoop框架和Mapreduce算法,能够快速有效地发现信号间的强关联关系。针对二次设备的缺陷特征,建立异常模型,提出遥信历史数据准备和清洗方法,滤除复归、抖动等噪声信号,并将字符串数据转换为以关键字为标识的事务数据项集。在此基础上采用分布式并行FP-growth算法挖掘各变电站历史数据库异常信号的频繁项集和强关联关系。应用结果表明,该方法能够有效地发现二次设备的频发异常,找到诱发异常的缺陷,为家族性缺陷的认定提供数据基础。

The monitoring data of smart substation devices is stored in a decentralized way.It is difficult to extract equipment defect features,so it is necessary to determine the association rules between device abnormal signals and defects by data distributed mining.Using a Hadoop framework and the MapReduce algorithm,a distributed parallel FP-growth algorithm can quickly and effectively find the strong correlation between signals.Given the defect characteristics of the secondary devices,an abnormal model is established.A method of preparing and cleaning the historical data of remote signals is proposed.This can filter out noise signals such as reset and jitter,and convert the string data into the key words data item set.The distributed parallel FP-growth algorithm is used to mine the frequent item sets and find strong correlation of abnormal signals in the historical database of each substation.The application results show that this method can effectively determine frequent abnormalities in the secondary devices and find the defects,providing the data basis for the identification of family defects.