Due to the limitation of frequency resolution and the spectrum leakage caused by signal windowing, the spectrums of harmonic and interharmonic components with close frequencies overlap each other. When the dense interharmonic (DI) components are close to the harmonic spectrum peaks, the harmonic phasor estimation accuracy is seriously reduced. To address this problem, a harmonic phasor estimation method considering DI interference is proposed in this paper. Firstly, based on the spectral characteristics of the dense frequency signal, the phase and amplitude characteristics are used to determine whether DI interference exists in the signal. Secondly, an autoregressive model is established by using the autocorrelation of the signal. Data extrapolation is performed on the basis of the sampling sequence to improve the frequency resolution and eliminate the interharmonic interference. Finally, the estimated values of harmonic phasor, frequency and rate of change of frequency are obtained. The simulation and some experimental results demonstrate that the proposed method can accurately estimate the parameters of harmonic phasors when DIs exist in the signal, and has a certain anti-noise capability and dynamic performance.

In large-scale applications, parts of harmonic data are inevitably lost during transmission. This study presents an approach for the recovery of missing harmonic data based on the spectral graph theory. The proposed methodology involves graph theory for constructing a Laplacian matrix and a graph signal reconstruction function, the merging K -means algorithm for building a priori information model, and the accelerated segmentation Bregman iterative algorithm for solving the reconstruction function. Compared with existing methods on data recovery in power systems, the method maintains a good recovery accuracy when the data correlation of measurement units is low and the prior information is little. The proposed method has good anti-noise performances and low computational complexity. The feasibility and accuracy of the proposed method are verified through simulation and field recorded data.

This paper presents a novel harmonic state estimation (HSE) methodology based on graph signal processing. The proposed approach can perform HSE without calculating harmonic impedance. The proposed methodology involves graph signal theory for constructing a graph Laplacian matrix and an unconstrained optimization function, the adaptive K ‐means algorithm for building a prior information model of the target bus, as well as data fuzzification and the domain averaging method for state estimation. Simulations are carried out using a 30‐bus power distribution system, and results are presented and discussed to prove the efficiency of the proposed method. The practical application further verifies the availability of the proposed method.

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.