电压暂降时空多粒度属性分析与知识发现方法

Spatial-Temporal Multi-granular Attribute Analysis and Knowledge Discovery Method for Voltage Sag

随着高端制造业的发展,以电能质量为代表的电力扰动已对敏感用户造成极大的经济损失,为了提升电网安全稳定水平并优化营商环境,针对电压暂降这一典型电力扰动事件,提出电压暂降时空多粒度属性分析与知识发现方法。由于传统电力扰动分析方法依赖机理分析与精确建模,难以处理包含多不确定因素的复杂问题,从复杂问题认知规律的角度出发,提出“数据—特征—指标—信息—知识”所构成的递进认知架构,作为解决复杂问题的一般思路。在此基础上,通过电压暂降时空多粒度属性分析对不同时空尺度下的暂降相关属性进行拓展,克服了传统方法仅从单一粒度分析问题造成暂降信息缺失的问题。由于不同时空粒度下的暂降属性变化反映了其他不确定性因素对电压暂降的影响,通过粒度约简挖掘监测数据背后蕴藏的电压暂降影响程度与电网结构属性之间的关联关系,推导发现电压暂降传播规律。通过仿真和实测数据对本文方法的有效性和可靠性进行了验证,本文方法可适用于包含多不确定因素的复杂问题,有助于突破电力扰动相关的诸多技术瓶颈。

In order to mitigate voltage sag related problems based on power quality monitoring data,it is meaningful to improve the efficiency of power quality data analysis.Due to depending on accurate models of voltage sag,the traditional methods are inadequate for complex problems with multiple uncertainty factors.Therefore,the spatial-temporal multi-granular attribute analysis of voltage sag data and a related knowledge discovery method were proposed in this paper.Inspired by the cognitive hierarchy of complex problems,a framework consisted of“data—characteristic—index—information—knowledge”was proposed as a general technical route for voltage sag related problems.Based on the framework,to solve the problem of information loss caused by single granular,sag information in different granular was extended by voltage sag spatial-temporal multi-granular analysis.The relationship between power system structure attribute and voltage sag was discovered by granular reduction.Then,knowledge about voltage sag severity and propagation was derived.The synthetic and measured data were used to validate the effectiveness of the proposed method.Results showed that the proposed method can describe and resolve complex problems with many uncertainty factors.