Physics-Guided Residual Learning for Probabilistic Power Flow Analysis
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, University of California at Santa Cruz, Santa Cruz, CA, USA
Funder
Faculty Research Grant of UC Santa Cruz
Hellman Fellowship
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10227264.pdf?arnumber=10227264
Reference49 articles.
1. Robust mapping rule estimation for power flow analysis in distribution grids
2. Probabilistic load flow calculation with quasi-Monte Carlo and multiple linear regression
3. A Data-Driven Approach to Linearize Power Flow Equations Considering Measurement Noise
4. Data-Driven Power Flow Linearization: A Regression Approach
5. Probabilistic Power Flow by Monte Carlo Simulation With Latin Supercube Sampling
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