A group sparse Bayesian learning algorithm for harmonic state estimation in power systems

In view of increasing concerns about climate change and global warming, various renewable energy sources are widely adopted in power systems to reduce greenhouse gas emissions. However, the widespread adoption of renewable energy sources exacerbates the problem of harmonics pollution. In order to improve power quality, it is urgent to develop a new approach for monitoring harmonics. By leveraging the structured sparsity and spatial sparsity of harmonic sources, this paper proposes a group sparse Bayesian learning method for solving harmonic state estimation problems. The proposed algorithm can employ a small number of measurements less than the number of state variables to pinpoint harmonic sources and to estimate the harmonic magnitudes and angles. Moreover, the proposed method achieves automatic hyperparameter adjustments and solves the system states in complex domain without splitting them into real and imaginary parts. Extensive results on a benchmark IEEE 14-bus system are presented to substantiate the superiority of the proposed method in terms of localization accuracy and harmonic magnitude and phase angle estimation.