Zhong, Qing, Liang, Jiahao, Xu, Zhong, Meyer, Jan, Wang, Longjun, and Wang, Gang
• The first contribution of our manuscript is: we overcomed the problem of poor practicability when the traditional clustering methods were applied for online analysis of PQ monitoring data with complex structures. The traditional clustering results of PQ monitoring data with imbalanced structure needs improvement for the real application. • The existing researches do not consider the imbalanced structure of data when applying clustering algorithm to PQ monitoring data. Quantum clustering is a more suitable clustering algorithm for analyzing data with imbalanced structure. It can effectively avoid the interference of subjective factors and recognize the patterns in large amounts of PQ monitoring data with complex structure. The clustering results can contribute to the effective application of PQ monitoring data. • Therefore, this contribution is novel. To the best of our knowledge, it has not been published in the existing literatures. • The second contribution of our manuscript is: multiple clusters with diverse characteristics are obtained and the patterns of clusters are analyzed from the PQ point of view. The cluster severity index is defined from the patterns in the clusters to represent the PQ performance in the clusters. The region severity index is defined from the PQ monitoring data of regions in different clusters, which represent the PQ performance of the regions. The region severity index is a relative value which can be used to compare the PQ performance in different regions. The indices we proposed enable an easy comparison among regions. Applied to the PQ monitoring data of 11 regions in a large city in China, the method can effectively distill diverse abnormal patterns from substantially imbalanced monitoring data, which is beneficial for easy and automatic detection of regions having issues with PQ indices exceeding defined limits. • Therefore, this contribution is novel and practical. To the best of our knowledge, it has not been published in the existing literatures. Steady-state power quality (PQ) indices seldom exceed the limits in power systems. Therefore, steady-state PQ monitoring data show a distinct imbalanced structure (i.e. unequal distribution). To ensure the efficient assessment of PQ performance, new and more appropriate tools for analyzing these data are needed. In this paper, an analysis method based on Quantum clustering is proposed, aiming to address the imbalanced structure of monitoring data. Firstly, the imbalanced structure of PQ monitoring data is analyzed. Secondly, Quantum clustering is performed on the PQ monitoring data and several disparate patterns of clusters are recognized. Thirdly, cluster severity index of cluster and the region severity index of region are defined to quantify the PQ performance of a cluster and a region, respectively. The cluster severity index is defined according to the sum of the proportions between cluster centers and limit for the respective PQ index. The region severity index is defined according to the proportions of PQ monitoring data of each region belonging to the different clusters with the corresponding cluster severity index. By sorting the region severity index in descending order, regions with poorer PQ performance become a higher-ranking place, which enables an easy comparison among regions. Finally, the proposed method is applied to the PQ monitoring data of 11 regions in a large city in China. The method can effectively distill diverse abnormal patterns from substantially imbalanced monitoring data, which is beneficial for easy and automatic detection of regions having issues with PQ indices exceeding defined limits. [ABSTRACT FROM AUTHOR]