scholarly journals Complex Variable Multi-phase Distribution System State Estimation Using Vectorized Code

2020 ◽  
Vol 8 (4) ◽  
pp. 679-688 ◽  
Author(s):  
Izudin Dzafic ◽  
Rabih Jabr ◽  
Tarik Hrnjic
Keyword(s):  
State Estimation ◽  
Complex Variable ◽  
Distribution System ◽  
Phase Distribution ◽  
System State ◽  
Multi Phase

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.

ADVANCED DISTRIBUTION SYSTEM STATE ESTIMATION IN MULTI‐AREA ARCHITECTURES

2020 ◽  
pp. 329-374
Author(s):  
Marco Pau ◽  
Paolo Attilio Pegoraro ◽  
Ferdinanda Ponci ◽  
Sara Sulis
Keyword(s):  
State Estimation ◽  
Distribution System ◽  
System State
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