Multi-Harmonic Sources Harmonic Contribution Determination Based on Data Filtering and Cluster Analysis

Accurately determining power consumer harmonic contribution determination is an effective method to solve power quality disputes and alleviate harmonic pollution of power grids. This paper proposes a multi-harmonic sources harmonic contribution determination algorithm based on data filtering and cluster analysis. Aiming at the problem of background harmonic fluctuation, this paper uses the cross-approximation entropy (CAE) algorithm to filter the effective data segments of the harmonic voltage and current at the point of common coupling (PCC) to avoid the interference caused by background harmonics. For the problem of harmonic impedance changes of system, using the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm to detect the harmonic impedance changes of the system. The harmonic contribution under different system harmonic impedance is calculated based on the data of each class cluster. The accuracy of the proposed method is improved compared with existing methods. The experimental analysis demonstrates the effectiveness and superiority of the algorithm.