Data-Driven Method for Missing Harmonic Data Completion

In large-scale applications, missing harmonic data during transmission is inevitable. This paper presents a novel approach for the completion of missing harmonic data based on a data-driven approach. The idea is to construct an adjacency matrix and unconstrained optimization function through spectra...

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Bibliographic Details
Published in:IEEE access Vol. 9; pp. 164037 - 164046
Main Authors: Xu, Rui, Ma, Xiaoyang, Zhou, Runze, Zhao, Jinshuai, Wang, Ying
Format: Journal Article
Language:English
Published: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">K -means
augmented Lagrange
Clustering algorithms
Current measurement
data completion
graph signal
Graph theory
Harmonic analysis
Iterative algorithms
Iterative methods
Measurement units
Measuring instruments
Missing harmonic data
Optimization
Phase measurement
Phasor measurement units
Phasors
Power harmonic filters
ISSN:2169-3536, 2169-3536
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Summary:In large-scale applications, missing harmonic data during transmission is inevitable. This paper presents a novel approach for the completion of missing harmonic data based on a data-driven approach. The idea is to construct an adjacency matrix and unconstrained optimization function through spectral graph theory, and construct an a priori information model of the target bus using the merging K-means algorithm. The augmented Lagrange iteration algorithm is used to iteratively solve the unconstrained optimization function, so to complete the missing harmonic data. In comparison with existing methods, this method reduces the data dependence between different phase measurement units and does not require the optimization strategy of phasor measurement units, which is suitable for single-phasor harmonic data completion. Finally, the effectiveness and practicability of the proposed method were verified using artificial and field data.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3132152