Integrated Power Quality Regionalization Control Method Based on Improved Gray Relational Analysis

In this paper, a comprehensive regionalization control method based on improved gray relational analysis (IGRA) was proposed to the superimposed power quality control for power system. With isometric important feature sequences preprocessed from the original data, the importance of the information content provided by each power quality indicator was quantified by entropy weight method. Combined the weight and dimensionless time series data, weighted summation method is used to generate the comprehensive sequence of each node. As an important parameter, the similarity of variation trend and numerical distance between reference sequence and comparative sequence is applied to IGRA algorithm to generate the gray relational coefficient which is used to figure out correlation degree for comprehensive data between two nodes. A regionalization strategy according to the correlation degree was defined to figure out the regionalization results and master nodes by hierarchical clustering method. With the proposed method, the regionalization results can be more accurate and reasonable. Then the power quality in each region can be improved by power quality control of the master nodes. Meanwhile, the power quality control device can be saved. The standard IEEE 14 bus system simulation was constructed to verify the effectiveness of the proposed method.