Abstract
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.
Highlights
In the field of electronic and electrical engineering, nonlinear load is widely used, which makes the harmonic pollution of three-phase symmetrical power system increasingly serious
When the background harmonic voltage fluctuations are small, there is no obvious similarity between the harmonic currents of the two harmonic sources and the harmonic voltage at the point of common coupling (PCC), and the comprehensive variable have a strong similarity with the harmonic voltage
Based on the selected data, the projection coefficient is computed from the linear regression method; the harmonic responsibility index can be calculated from Equation (2)
Summary
In the field of electronic and electrical engineering, nonlinear load is widely used, which makes the harmonic pollution of three-phase symmetrical power system increasingly serious. The first three methods have shown a higher accuracy in estimating the harmonic impedance, but they all require accurate harmonic waveform sampling data to obtain the harmonic amplitude and phase angle to realize the separation and calculation of the real and imaginary parts. Responsibility division for multi-harmonic sources has been widely studied, and the proposed methods are generally based on accurately estimating the harmonic impedance [21]. Due to the limitation on communication channels and storage capacity, the harmonic phase angle information is usually not stored in existing regional power quality online monitoring systems. To this end, to apply those methods, harmonic measuring instruments have to be used to conduct special tests on site to obtain short-term measurement data. Taking the linear regression method as an example, we discuss three typical data analysis methods
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