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

Minh-Quan Tran, Ahmed Zamzam, Phuong Nguyen, Guus Pemen

Research output: Contribution to journalArticlepeer-review

16 Scopus Citations


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 languageAmerican English
Article number3025
Number of pages13
Issue number11
StatePublished - 2021

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

NREL Publication Number

  • NREL/JA-5D00-80067


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


Dive into the research topics of 'Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks'. Together they form a unique fingerprint.

Cite this