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

Minh-Quan Tran, Ahmed Zamzam, Phuong Nguyen

Research output: Contribution to conferencePaperpeer-review

7 Scopus Citations


Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classic state estimation algorithms. In this paper, a new method, called the 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 three-phase 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 classic weighted least squares (WLS). Numerical results show that P2N2 outperforms WLS in terms of data redundancy and estimation accuracy.

Original languageAmerican English
Number of pages5
StatePublished - 28 Jun 2021
Event2021 IEEE Madrid PowerTech, PowerTech 2021 - Madrid, Spain
Duration: 28 Jun 20212 Jul 2021


Conference2021 IEEE Madrid PowerTech, PowerTech 2021

Bibliographical note

See NREL/CP-5D00-79183 for preprint

NREL Publication Number

  • NREL/CP-5D00-80833


  • Distribution system state estimation
  • phasor measurement unit
  • physics- aware neural network


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