基于梯形云模型的电能质量数据关联性挖掘方法

QU Guanglong

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基于梯形云模型的电能质量数据关联性挖掘方法

更新时间：2025-08-13

- 基于梯形云模型的电能质量数据关联性挖掘方法
- Automation of Electric Power Systems Vol. 39, Issue 7, Pages: 145-150(2015)
- 作者机构：
  
  四川大学电气信息学院,四川省,成都市,610065
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2015
- 稿件说明：
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QU Guanglong. [J]. Automation of electric power systems, 2015, 39(7): 145-150.
DOI：

QU Guanglong. [J]. Automation of electric power systems, 2015, 39(7): 145-150. DOI：

摘要

提出了一种基于梯形云模型的电能质量数据关联性挖掘方法。利用梯形云对电能质量指标进行等级概念区间划分

有效集成随机性和模糊性

达到软化区间边界目的。定义了基于梯形云的支持度和置信度计算公式

从电能质量数据库中挖掘关联规则

并配合使用Kulczynski量度和不平衡比作进一步相关性分析

过滤掉无意义的关联规则。通过对电能质量数据之间的关联性进行挖掘分析

可以发现电网中隐藏的运行特性

为管理部门制定决策提供有价值的信息。实例分析验证了所提方法的实用性和有效性。

Abstract

A correlation mining method for power quality data based on the trapezoidal cloud model is proposed. The division of level concept section with trapezoidal cloud model is able to soften the section boundaries by integration of randomness and fuzziness. The formulas for calculating the support and confidence based on the trapezoidal cloud model are defined for use in mining association rules from the power quality database. The correlation analysis with Kulczynski measurement and imbalance ratio will filter out senseless association rules. Through the mining and analysis of power quality data correlation

the operation characteristics hidden in the power grid can be found

and valuable information provided for decision-making by management. An example has verified the practicability and validity of the method proposed.

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