A data-driven power flow method for radial networks achieves voltage magnitude predictions with error below 0.001 p.u. using historical data and optimal sensor placement with only 25% of locations instrumented.
Branch Flow Model: Relaxations and Convexification—Part I,
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Data-Driven Power Flow for Radial Distribution Networks with Sparse Real-Time Data
A data-driven power flow method for radial networks achieves voltage magnitude predictions with error below 0.001 p.u. using historical data and optimal sensor placement with only 25% of locations instrumented.