Robust State Estimation in Distribution Networks

被引：0
作者：
Brinkmann, Bernd [1 ]
Negnevisky, Michael
机构：
[1] Univ Tasmania, Sch Engn, Hobart, Tas, Australia
来源：
PROCEEDINGS OF THE 2016 AUSTRALASIAN UNIVERSITIES POWER ENGINEERING CONFERENCE (AUPEC) | 2016年
关键词：
Distribution network state estimation; load uncertainty; uncertainty quantification;
D O I：
暂无
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：
In this paper a new approach to state estimation in distribution networks is proposed. This approach is more robust against large uncertainties of the state estimation inputs than the conventional method. Traditionally, the goal of state estimation was to estimate the exact value of network parameters, such as voltages and currents. This works well in transmission networks where many real time measurements are available. In distribution networks, however, only few real-time measurements are available. This means that the estimated state can be significantly different from the actual network state. Therefore, the focus of the proposed robust state estimation is shifted from estimating the exact values of the network parameters to the confidence that these parameters are within their respective constraints. This approach is able to provide useful results for distribution network operation, even if large uncertainties are present in the estimated network state.
引用
收藏
页数：5
相关论文
共 50 条
[41]   Unsupervised Consensus Clustering of Acoustic Emission Time-Series for Robust Damage Sequence Estimation in Composites [J].
Ramasso, Emmanuel ;
Placet, Vincent ;
Boubakar, Mohamed Lamine .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2015, 64 (12) :3297-3307
[42]   Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook [J].
Di, Xuan ;
Shi, Rongye ;
Mo, Zhaobin ;
Fu, Yongjie .
ALGORITHMS, 2023, 16 (06)
[43]   Uncertainty quantification in LV state estimation under high shares of flexible resources [J].
Muller, Nils ;
Chevalier, Samuel ;
Heinrich, Carsten ;
Heussen, Kai ;
Ziras, Charalampos .
ELECTRIC POWER SYSTEMS RESEARCH, 2022, 212
[44]   Model Error, Information Barriers, State Estimation and Prediction in Complex Multiscale Systems [J].
Majda, Andrew J. ;
Chen, Nan .
ENTROPY, 2018, 20 (09)
[45]   Coherent Blending of Biophysics-Based Knowledge with Bayesian Neural Networks for Robust Protein Property Prediction [J].
Nisonoff, Hunter ;
Wang, Yixin ;
Listgarten, Jennifer .
ACS SYNTHETIC BIOLOGY, 2023, 12 (11) :3242-3251
[46]   Uncertainty Quantification through Dropout in Time Series Prediction by Echo State Networks [J].
Atencia, Miguel ;
Stoean, Ruxandra ;
Joya, Gonzalo .
MATHEMATICS, 2020, 8 (08)
[47]   Uncertainty Quantification of State Estimation in Nonlinear Structural Systems with Application to Seismic Response in Buildings [J].
Erazo, Kalil ;
Hernandez, Eric M. .
ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART A-CIVIL ENGINEERING, 2016, 2 (03)
[48]   Mechanical state estimation with a Polynomial-Chaos-Based Statistical Finite Element Method [J].
Narouie, Vahab ;
Wessels, Henning ;
Cirak, Fehmi ;
Roemer, Ulrich .
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2025, 441
[49]   Optimization via Statistical Emulation and Uncertainty Quantification: Hosting Capacity Analysis of Distribution Networks [J].
Du, Hailiang ;
Sun, Wei ;
Goldstein, Michael ;
Harrison, Gareth P. .
IEEE ACCESS, 2021, 9 :118472-118483
[50]   Gaussian Copula Methodology to Model Photovoltaic Generation Uncertainty Correlation in Power Distribution Networks [J].
Palahalli, Harshavardhan ;
Maffezzoni, Paolo ;
Gruosso, Giambattista .
ENERGIES, 2021, 14 (09)
12345