Assessing the Variable Harmonic Impedance Based on Bayesian Linear Regression

The existing approaches for assessing the utility harmonic impedance (UHI) cannot deal with the variable UHI. In fact, there are a variety of factors that may cause variations in UHI, including capacitor switching, filter switching, and operation adjustments in power grids. In regard to this problem, we propose a novel noninvasive approach for the estimation of UHI based on the Bayesian linear regression (BLR) that allows the variations of UHI to be precisely tracked. The effectiveness of the proposed method is verified by numerical simulations, and the results show that the performance of the proposed approach is much better than the existing approaches in tracking UHI with large-scale variations.

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Publication Details

Published
2024-12-13
DOI
https://doi.org/10.1109/icpe64565.2024.10929213
Primary Topic
Blind Source Separation Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Assessing the Variable Harmonic Impedance Based on Bayesian Linear Regression

Qin Shu, Hongru Zheng, Fangwei Xu, Kai Liu et al.
Blind Source Separation Techniques
article

Assessing the Variable Harmonic Impedance Based on Bayesian Linear Regression

Qin Shu, Hongru Zheng, Fangwei Xu, Kai Liu, Chuan Wang, Kai Guo
article en

Abstract

The existing approaches for assessing the utility harmonic impedance (UHI) cannot deal with the variable UHI. In fact, there are a variety of factors that may cause variations in UHI, including capacitor switching, filter switching, and operation adjustments in power grids. In regard to this problem, we propose a novel noninvasive approach for the estimation of UHI based on the Bayesian linear regression (BLR) that allows the variations of UHI to be precisely tracked. The effectiveness of the proposed method is verified by numerical simulations, and the results show that the performance of the proposed approach is much better than the existing approaches in tracking UHI with large-scale variations.

Sichuan University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Sichuan Province
Openalex Percentile: Top 68%
Blind Source Separation Techniques
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