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

Minh-Quan Tran, Ahmed S. Zamzam, Phuong H. Nguyen and Guus Pemen
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Minh-Quan Tran: Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
Ahmed S. Zamzam: National Renewable Energy Laboratory, Golden, CO 80401, USA
Phuong H. Nguyen: Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
Guus Pemen: Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands

Energies, 2021, vol. 14, issue 11, 1-13

Abstract: 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.

Keywords: distribution system state estimation (DSSE); pruned physics-aware neural network (P2N2); phasor measurement unit (PMU); data-driven modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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