利用数据融合对电能质量干扰的根本原因进行分类

Shanyi Xie, Fei Xiao, Q. Ai, Gang Zhou
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引用次数: 3

摘要

本文提出了一种基于数据融合技术的电能质量干扰原因分类建模框架。首先,考虑扰动能量的时间传播,采用移动窗口技术对扰动周期进行聚类;其次,采用实体匹配的方法对PQ扰动测量、设备切换动作数据和报警事件进行整合;然后,设计分布式关联规则挖掘,在集成数据中获得描述PQ特征与事件原因之间关系的强关联规则。分析结果具有良好的泛化性能。最后,以实际网格数据为例,验证了所提方法的有效性和实用性。测试结果表明,该方法能有效分析典型PQ扰动特征与事件原因之间的关系。这种关系对提高电能质量具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Underlying Causes of Power Quality Disturbances Using Data Fusion
This paper develops a data fusion technology based modeling framework for classifying the underlying cause of power quality (PQ) disturbances. First, the moving-window technique is used to cluster disturbance period with the consideration of the temporal propagation of disturbance energy. Secondly, the PQ disturbance measurements, equipment switching action data and alarm events are integrated by utilizing entity matching method. Then, the distributed mining of association rules is designed to obtain strong association rules within integrated data for describing the relationship between PQ features and event causes. The analysis results have good generalization performance. Finally, the real grid data were taken as an example to verify the effectiveness and practicability of the proposed method. The test results show that the proposed method can analyze the relationship between the typical PQ disturbance features and event causes effectively. This relationship is meaningful for power quality improvement.
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