Bayesian Learning-Based Harmonic State Estimation in Distribution Systems With Smart Meter and DPMU Data

• Published in: IEEE transactions on smart grid Vol. 11; no. 1; pp. 832 - 845
• Main Authors: Zhou, Wei, Ardakanian, Omid, Zhang, Hai-Tao, Yuan, Ye
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Admittance; Bayesian analysis; Computer simulation; Current measurement; Distributed generation; Electric power distribution; Energy sources; Harmonic analysis; harmonic state estimation; Integrated circuit modeling; load forecasting; Load modeling; Machine learning; Measuring instruments; Phasor measurement units; power distribution; Power flow; Power system harmonics; Recurrent neural networks; Regression analysis; Smart meters; State estimation; supervised learning
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2019.2938733

This paper studies the problem of locating harmonic sources and estimating the distribution of harmonic voltages in unbalanced three-phase power distribution systems. We develop an approach for harmonic state estimation utilizing two types of measurements from smart meters and distribution-level phasor measurement units (DPMUs). It involves regression analysis for power flow calculation, prediction of demands using recurrent neural networks, and sparse Bayesian learning for state estimation. The proposed approach requires fewer DPMUs than nodes, making it more applicable to existing distribution grids. We show the effectiveness of the proposed estimator through extensive numerical simulations on an IEEE test feeder. We also investigate how the increased penetration of distributed energy resources could affect the performance of our state estimator.