Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks: Preprint

Minh-Quan Tran, Ahmed Zamzam, Phuong Nguyen

Research output: Contribution to conferencePaper


Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classical state estimation algorithms. In this paper, a new method so-called pruned physics-aware neural network (P2N2) is developed to improve the voltage estimation accuracy in the distribution system. The method relies on the physical grid topology, which is used to design the connections between different hidden layers of a neural network model. To verify the proposed method, a numerical simulation based on one-year smart meter data of load consumptions for threephase power flow is 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 classical weighted least squares (WLS). Numerical results show that P2N2 outperforms WLS, in terms of data redundancy and estimation accuracy.
Original languageAmerican English
Number of pages9
StatePublished - 2021
Event2021 IEEE PowerTech Conference -
Duration: 28 Jun 20212 Jul 2021


Conference2021 IEEE PowerTech Conference

Bibliographical note

See NREL/CP-5D00-80833 for paper as published in proceedings

NREL Publication Number

  • NREL/CP-5D00-79183


  • distribution systems state estimation
  • machine learning
  • physics-aware neural networks


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