Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks
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.
- Research Organization:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Laboratory Directed Research and Development (LDRD) Program
- Grant/Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1788430
- Report Number(s):
- NREL/JA-5D00-80067; MainId:42270; UUID:1c59d26d-7c07-4b3e-bbe6-1a0ceef92135; MainAdminID:24591
- Journal Information:
- Energies (Basel), Vol. 14, Issue 11; ISSN 1996-1073
- Publisher:
- MDPI AGCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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