End-to-end neural network training of pseudo-measurements coupled to a classical WLS state estimator improves DSSE accuracy on IEEE test systems under limited observability.
Arobustdata- driven gaussian process regression-enabled distribution system state estimation using partial node data
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End-to-End Pseudo-Measurement Learning for State Estimation under Limited Observability
End-to-end neural network training of pseudo-measurements coupled to a classical WLS state estimator improves DSSE accuracy on IEEE test systems under limited observability.