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

Minh-Quan Tran (Corresponding author), Ahmed S. Zamzam, Phuong H. Nguyen, A.J.M. (Guus) Pemen

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)
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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.
Original languageEnglish
Article number3025
Number of pages13
Issue number11
Publication statusPublished - 24 May 2021


  • distribution system state estimation (DSSE)
  • pruned physics-aware neural network (P2N2)
  • phasor measurement unit (PMU)
  • data-driven modeling
  • Data-driven modeling
  • Pruned physics-aware neural network (P2N2)
  • Distribution system state estimation (DSSE)
  • Phasor measurement unit (PMU)


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