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

Abstract

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.

Harmonic indices for continuous evaluation of consumer's harmonic contribution

“…To confirm the accuracy of the results of the proposed algorithm, the results of the proposed algorithm were compared to real values of the network calculated by impedance characteristic curve in DIgSILENT on the one hand and to those obtained by the method of HC online tracking at the PCC of harmonic sources presented in [4], on the other hand. The accuracy of the proposed method in multi-point HC calculation is confirmed by comparing the results with [5].…”

“…Different methods are presented to determine the harmonic contribution (HC) of consumers which can be divided into two general groups of single-point methods (related to the connection point of the participant to the network) and multi-point methods (analysis of the participant's effect on different points of the network) [5][6][7][8]. Most multi-point methods require the existence of simultaneous data from various points of the network; however, the single-point methods do not have such a requirement.…”

“…In addition, the artificial intelligence-based methods including neural network [20], fuzzy system [9], and the data classification [5,21,22], have also been used for HC calculation. The harmonic disturbances in power networks were analysed and studied by the preparation of standards and measurements as well as the assessment of disturbances at various points.…”

Study on Harmonic Impedance Estimation and Harmonic Contribution Evaluation Index

“…It can be seen from Fig.8 that the red part of the scatter diagram is distributed in the shape of three long strips corresponding to three possible utility impedance values. Firstly, K-means, spectrum clustering and DBSCAN (proposed in [19]) are used to cluster the scatter diagram, and the clustering results are shown in Fig.9.…”

“…However, the detection of abrupt change points may be interfered by large background harmonic voltage fluctuations. In [19], the DBSCAN method is used to distinguish the data segments corresponding to the different utility impedance values, but DBSCAN is too sensitive to the initial parameters, which is not conducive to obtaining correct results.…”