基于云计算技术的电力大数据预处理属性约简方法

针对大数据时代下电网数据体量大、类型多、速度快的特点,传统的属性约简方法已经无法完成对电力大数据的预处理,为此提出一种基于云计算技术的电力大数据预处理属性约简方法。该方法剖析了粗糙集中相对正域理论的特性,利用MapReduce模型设计了可以并行计算正域中元素个数的属性约简算法MP_POSRS。最后,在Hadoop平台上对电网故障诊断表和风电实测数据进行属性约简,实验结果表明,该方法有效可行,并具有较好的加速比和可扩展性,适用于电力大数据预处理属性约简。

An Attribute Reducing Method for Electric Power Big Data Preprocessing Based on Cloud Computing Technology

In face of the conventional attribute reduction incapability of grid data preprocessing for its big volume, diversified types and high speed in the forthcoming age of big data, a new method of electric power big data preprocessing via attribute reduction based on cloud computing technology is put forward. The characteristics of the relative positive region theory of the rough set is analyzed, and a parallel attribute reducing algorithm named MP_POSRS that can calculate the number of elements in the relative positive region is designed by taking good advantages of the MapReduce model in this method. Finally, the experiments including operations on the decision table of power grid fault diagnosis and real data of wind power are performed on a Hadoop platform, the results showing that the method is effective and feasible for dealing with power grid big data, and with good speedup and scalability required by electric power big data preprocessing via attribute reduction.