Low voltage system state estimation using smart meters

2012 ◽  
Author(s):  
Ahmad Abdel-Majeed ◽  
Martin Braun
Keyword(s):  
State Estimation ◽  
Low Voltage ◽  
System State ◽  
Smart Meters

scholarly journals Smart meters as a key component of modern measuring infrastructure providing observability and state estimation of low-voltage distribution networks

2018 ◽  
Vol 69 ◽  
pp. 02012
Author(s):  
Yana Kuzkina ◽  
Irina Golub
Keyword(s):  
State Estimation ◽  
Low Voltage ◽  
Distribution Network ◽  
Distribution Networks ◽  
The State ◽  
Voltage Distribution ◽  
Smart Meters ◽  
The Real ◽  
Real Distribution

The paper presents a solution to the problem of organization of a system for collecting and transmitting information about measurements from smart meters necessary for the state estimation of a low-voltage distribution network. The problems of providing the sufficiency of measurements for the observability of the network and the influence of errors in the information about load connection to phases on the quality of the observability are considered. The results of allocation of smart meters and the state estimation of the real distribution network are given.

EVALUATION OF A THREE-PHASE DISTRIBUTION SYSTEM STATE ESTIMATION FOR OPERATIONAL USE IN A REAL LOW VOLTAGE GRID

2021 ◽  
Author(s):  
Heiner Früh ◽  
Krzysztof Rudion ◽  
Alix von Haken ◽  
Daniel Groß ◽  
Bartholomäus Wasowicz
Keyword(s):  
State Estimation ◽  
Distribution System ◽  
Low Voltage ◽  
Phase Distribution ◽  
System State ◽  
Three Phase

scholarly journals Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3025
Author(s):  
Minh-Quan Tran ◽  
Ahmed S. Zamzam ◽  
Phuong H. Nguyen ◽  
Guus Pemen
Keyword(s):  
State Estimation ◽  
Distribution System ◽  
Phase Distribution ◽  
State Of The Art ◽  
Weighted Least Squares ◽  
Computational Cost ◽  
Estimation Accuracy ◽  
System State ◽  
Smart Meters ◽  
Distribution Grids

The development of active distribution grids requires more accurate and lower computational cost state estimation. In this paper, the authors investigate a decentralized learning-based distribution system state estimation (DSSE) approach for large distribution grids. The proposed approach decomposes the feeder-level DSSE into subarea-level estimation problems that can be solved independently. The proposed method is decentralized pruned physics-aware neural network (D-P2N2). The physical grid topology is used to parsimoniously design the connections between different hidden layers of the D-P2N2. Monte Carlo simulations based on one-year of load consumption data collected from smart meters for a three-phase distribution system power flow are developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares and state-of-the-art learning-based DSSE approaches. Numerical results show that the D-P2N2 outperforms the state-of-the-art methods in terms of estimation accuracy and computational efficiency.

Distribution System State Estimation Based on Nonsynchronized Smart Meters

2015 ◽  
Vol 6 (6) ◽  
pp. 2919-2928 ◽  
Author(s):  
Arash Alimardani ◽  
Francis Therrien ◽  
Djordje Atanackovic ◽  
Juri Jatskevich ◽  
Ebrahim Vaahedi
Keyword(s):  
State Estimation ◽  
Distribution System ◽  
System State ◽  
Smart Meters

scholarly journals The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5363
Author(s):  
István Táczi ◽  
Bálint Sinkovics ◽  
István Vokony ◽  
Bálint Hartmann
Keyword(s):  
State Estimation ◽  
Distribution System ◽  
Distribution Systems ◽  
Low Voltage ◽  
Early Stage ◽  
Renewable Energy Sources ◽  
General Structure ◽  
Theory And Practice ◽  
System State ◽  
Time Operation

Global trends such as the growing share of renewable energy sources in the generation mix, electrification, e-mobility, and the increasing number of prosumers reshape the electricity value chain, and distribution systems are necessarily affected. These systems were planned, developed, and operated as a passive structure for decades with low level of observability. Due to the increasing number of system states, real time operation planning and flexibility services are the key in transition to an active grid management. In this pathway, distribution system state estimation (DSSE) has a great potential, but the real demonstration of this technique is in an early stage, especially on low-voltage level. This paper focuses on the gap between theory and practice and summarizes the limits of low-voltage DSSE implementation. The literature and the main findings follow the general structure of a state estimation process (meter placement, bad data detection, observability, etc.) giving a more essential and traceable overview structure. Moreover, the paper provides a comprehensive mapping of the possible use-cases state estimation and evaluates 27 different experimental sites to conclude on the practical applicability aspects.

scholarly journals Methods of linear and nonlinear state estimation of distribution network

2018 ◽  
Vol 58 ◽  
pp. 03010
Author(s):  
Irina Golub ◽  
Evgeny Boloev
Keyword(s):  
State Estimation ◽  
Low Voltage ◽  
Distribution Network ◽  
Quadratic Function ◽  
Smart Meters ◽  
Nonlinear State Estimation ◽  
Estimation Of Distribution ◽  
Linear And Nonlinear ◽  
Linear State ◽  
Nonlinear State

The paper proposes a new approach to the problem of state estimation of a low voltage distribution network by the measurements coming from smart meters. The problem of nonlinear state estimation based on the measurements of nodal powers and voltages is solved by the method of simple iteration which minimizes the quadratic function of the residues with and without the consideration of the constraint on the zero currents in the transit nodes. The same algorithms are proposed to use for linear state estimation based on the measurements of nodal currents and voltages. The effectiveness of the proposed methods for linear and nonlinear state estimation is illustrated on the 33 nodes three-phase four-wire low-voltage network.

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