基于改进决策树的配电网多源数据快速检索

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.

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

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 multi-source heterogeneous information extraction of power distribution network, effectively avoid duplicate data, and meet the requirements of online engineering decision.