基于数据分析的配电网故障数据特征变量提取

Characteristic variable extraction of fault data in distribution feeder based on data analysis

配电网线路故障预测是提升配电网可靠性指标的重要手段,为了构建性能稳定、预测能力强的线路故障预测模型,需要保证模型输入特征变量的有效性、强相关性和无冗余性。为合理确定线路故障预测模型的输入特征变量,本文采用数据探索和挖掘的分析方法对馈线故障及其影响因素之间的关系进行了分析研究,以皮尔森相关系数为计算指标,对大量实际馈线故障数据与其影响因素进行相关性统计,从馈线故障的时间-地域特性、外部影响因素、自相关特性、运行影响因素等四个维度筛选出了馈线故障影响因素特征变量作为馈线故障预测模型的输入变量,直观有效地剔除无关故障特征变量。因此,所提出方法可用于配电网大数据的预处理分析和提取,为配电网故障预测提供重要方法和数据基础。

Distribution feeder fault prediction is an important way to improve the distribution network reliability, in orter to construct a feeder fauh prediction model with stable performance and strong forecasting ability, it is necessary to ensure the validity, strong correlation and non - redundancy of the model input feature variables. For reasonable determine the characteristics of input variables of the feeder fauh prediction model, this article adopts the analysis method of data exploration and mining of the relationship between the feeder fauh and its influencing factors are analyzed, Specifically, through correlation analysis of a large nmnber of actual feeder fauh data based on Pearson Correlation Coefficient, The con＇elation statistics of large number of actual feeder fault data and its influencing factors were carried out. From four dimensions of feeder fauh such as time - regional characteristics, external influencing factors, selfinfluencing factors, and operation influencing factors, the characteristic variables of feeder fault influence factors are selected as input variables of feeder fault prediction model, The independent fault feature variables are eliminated intuitively and effectively. Therefore, the proposed method can be used for pretreatment analysis and extraction of big data of distribution network, which provides important method and data base for distribution network fault prediction.