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In the power system, the harmonic is one of the important concerns of power quality, which has a significant impact on the safe operation of power grids and the energy efficiency for users. When there are not enough special harmonic monitoring devices installed on public transport, it is important to identify the harmonic source and assess the harmonic contribution based on the load data of the user's electricity meter. This paper proposes a data selection and matching idea based on change‐point monitoring (CPM), canonical correlation analysis (CCA) and dynamic adjusted shape context (DASC) for determining the harmonic contributions of individual customers and utilities. In the proposed approach, the CPM is used to select the valuable data by detecting the violent fluctuation area of harmonic voltage and the DASC to determine the harmonic contribution of individual customers by matching the user load sequence and harmonic voltage sequence based on CCA, filtering the harmonic contribution from the utility side. Simulation data is used to evaluate the performance of the method and a real‐world case study is also conducted. It is found that the proposed technique can be used to reveal the harmonic impact of the loads if data is selected resonably.

The variable background harmonic data and incomplete phasor information make multi-harmonic source responsibility division in three-phase symmetrical power system a significant challenge. In this paper, a background harmonic data selection method based on canonical correlation analysis is proposed to deal with multi-harmonic source responsibility division without phasor information. Firstly, the canonical correlation coefficient between harmonic voltage and harmonic current is used to characterize the fluctuations of background harmonic voltage. Then, the sliding window method is adopted to select the harmonic voltage and harmonic current with small fluctuations. Next, the canonical correlation results for selected data are used to calculate the harmonic responsibility index via the linear regression method. The harmonic responsibility index in the form of percentage represents the harmonic responsibility division. Finally, several experimental results demonstrate that the proposed method has a high accuracy in calculating the harmonic responsibility division, particularly when the user side contains fluctuations of unknown harmonic sources.