The widespread deployment of renewable energies and non-linear loads has led to serious harmonic pollution in electrical distribution networks. Evaluation of the harmonic contribution (HC) of each customer is a significant task for power quality management. Most previous studies focus on periodic evaluation methods

where numerous data have to be collected in advance over a period (e.g.

one day). However

customer behaviors are time-varying and would lead to dynamic HCs

which can not be captured by traditional periodic evaluation methods. To address this issue

this paper presents a novel real-time HC evaluation method considering multiple dynamic customers. First

a two-stage iteration estimator is proposed based on the information fusion technique to quantify real-time HC of each customer. Then

to mitigate the negative effect of unknown background harmonics

a dominant index method is developed to determine credibility of the measurement data. On this basis

an adaptive gain selection strategy is proposed to improve accuracy of real-time HC evaluation. By doing so

the major harmonic contributor can be identified for implementing harmonic suppression and improving power quality. Finally

a typical IEEE system is utilized to verify the proposed methods. The results show that using the proposed method

evaluation errors can be reduced from about 10% to 2.5%. Moreover

the total harmonic distortion of voltage can be suppressed from 5.564% to 0.702%. Therefore

this research provides practical insights for addressing harmonic problems in power systems.