基于历史信息挖掘的变压器健康状态聚类方法

传统变压器健康状态评估主要集中在评价导则与模型建立上,然而人为因素与低数据利用率或导致评估结果不准确,对此提出了一种基于历史信息挖掘的变压器健康状态聚类方法。首先利用关联分析挖掘变压器历史信息,以置信度量化评价指标。其次采用主分量分析方法获取评价指标关联权重,据此修正指标聚类空间。最后通过Canopy-kmeans两层聚类方法分析变压器集群健康状态,针对不同簇给出相应健康等级以指导状态检修与运行调度。算例分析验证了该方法的可行性与有效性。

A clustering method for transformer health state based on historical information mining

Traditional transformer health state assessment is mainly focused on evaluation guideline and mathematical model establishment. However, the assessment results may be inaccurate due to human factors and low data utilization. A clustering method for transformer health state based on historical information mining is proposed to solve these problems. First, association analysis method is applied to extract transformer historical information and quantify evaluation indexes with confidence level. Then association weights of evaluation indexes are obtained from principal component analysis to correct index clustering space. Finally, Canopy-kmeans two-layer clustering method is utilized to analyze transformers health state and assign corresponding health ratings to different clusters to guide the condition-based maintenance and operation dispatching. The feasibility and effectiveness of the proposed method are verified in case study. This work is supported by National High-tech R & D Program of China (863 Program) (No. 2015AA050204).