Harmonic current estimation of unmonitored harmonic sources with a novel oversampling technique for limited datasets

In modern power systems, harmonics are amongst the significant issues attributed to renewable energy sources and nonlinear loads. Direct harmonic monitoring of the entire power system may be too costly or impractical, and measured data could be limited. In this paper, a new methodology is proposed to estimate harmonic current rms values of unmonitored harmonic sources, based on harmonic voltage rms magnitudes only, measured at a limited number of monitored buses. A new technique of output curve-normalization is employed in pre-processing. Subsequently, a method is proposed to refine the architecture of the Artificial Neural Networks' (ANNs), after which ANN-based harmonic current estimators are developed for each harmonic order and each harmonic source. Furthermore, a novel Neural Oversampling Consensus Algorithm for Regression (NOCAR) is proposed to improve estimation accuracy. K-Nearest Neighbor (KNN) and ANN are combined in developing NOCAR. A comparison is made with state-of-the-art techniques by using synthetic data, which demonstrates both the proposed method's robustness and its capability to perform when minimal information is available. The implementation for real data demonstrates the efficiency of the ANN-based harmonic current estimators with oversampling. The influence of the number of harmonic meters is investigated, revealing the ability of this data-driven technique to reduce the number of harmonic meters, and hence monitoring costs. Moreover, the correlation between different harmonic orders is studied, with results suggesting that, unlike the widely accepted notion, this correlation should not be ignored in harmonic analysis. This study highlights the advantages of integrating intelligent techniques into harmonic monitoring systems.