Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
