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Found 6 papers, 0 papers with code
Mitigating Smart Meter Asynchrony Error Via Multi-Objective Low Rank Matrix Recovery
Smart meters (SMs) are being widely deployed by distribution utilities across the U. S.
A Hierarchical Deep Actor-Critic Learning Method for Joint Distribution System State Estimation
To maintain monitoring accuracy, the two levels exchange boundary information with each other at the secondary nodes, including transformer voltages (first layer to second layer) and active/reactive total power injection (second layer to first layer).
Computational Efficiency
Hierarchical Reinforcement Learning
Multi-Source Data Fusion Outage Location in Distribution Systems via Probabilistic Graph Models
A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account using a probabilistic graphical method.
Enriching Load Data Using Micro-PMUs and Smart Meters
The key to our approach is to statistically recover the high-resolution load data, which is masked by the low-resolution data, using trained probabilistic models of service transformers that have both high and low-resolution data sources, i. e, micro-PMUs and smart meters.
Disaggregating Customer-level Behind-the-Meter PV Generation Using Smart Meter Data and Solar Exemplars
In most cases, PVs are installed behind-the-meter (BTM), and only the net demand is recorded.
Learning-Based Real-Time Event Identification Using Rich Real PMU Data
A large-scale deployment of phasor measurement units (PMUs) that reveal the inherent physical laws of power systems from a data perspective enables an enhanced awareness of power system operation.
