基于改进决策树的配电网多源数据快速检索

Fast Multi-Source Data Retrieval Method for Distribution Network Based on Improved Decision Tree

当前,电网中含有海量的多源信息数据,但是由于数据体量大、种类多、维度高,难以实现高效有效的数据检索.因此本文根据实际电力运行系统的数据结构及多源数据库样本分析,提出了一种基于互信息的改进决策树算法作为数据挖掘内核,并提出适用于电力系统的并行处理架构,可实现多源数据的快速、有效信息检索,并有效处理实时数据.在搜索时根据代表性特征子集直接从多源信息原始数据提取信息,判断索引信息量并排序形成决策树模型,通过Spark MapReduce Python数据分解并行检索实现多源数据同时提取,缩短检索时间.本文以某区域电网数据库为算例进行模拟验证,结果表明:该方法可以实现配电网的多源异构信息提取,有效避免重复数据,满足在线工程决策要求.

At present,the power grid contains a large number of multi-source information data,but due to the large size of the data types and high multi-dimensions,it is difficult to achieve effective data retrieval.According to the data structure of actual power operation system and multi-source database sample analysis,an improved decision tree algorithm based on mutual information is proposed as the kernel of data mining,and a parallel processing architecture suitable for power system is put forward,which can retrieve multi-source data fast and efficiently.The information is directly extracted from the original data of multi-source information according to the representative feature subset during searching.The index information is judged and sorted to form the decision tree model,and multi-source data is extracted simultaneously through Spark MapReduce Python data decomposition and parallel retrieval,so as to shorten the retrieval time.Taking a regional power grid database as an example to simulate and verify,the results show that the method can realize multisource heterogeneous information extraction of power distribution network,effectively avoid duplicate data,and meet the requirements of online engineering decision.