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

被引:73
作者
Zhou, Wei [1 ,2 ]
Ardakanian, Omid [3 ]
Zhang, Hai-Tao [1 ,2 ]
Yuan, Ye [1 ,2 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China
[2] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China
[3] Univ Alberta, Dept Comp Sci, Edmonton, AB T6G 2E8, Canada
基金
中国国家自然科学基金;
关键词
Harmonic analysis; Load modeling; Smart meters; Admittance; Integrated circuit modeling; Phasor measurement units; Current measurement; Power system harmonics; harmonic state estimation; load forecasting; supervised learning; power distribution; POWER NETWORKS; KALMAN FILTER; IDENTIFICATION; PREDICTION;
D O I
10.1109/TSG.2019.2938733
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:832 / 845
页数:14
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