Anomaly detection in distributed electrical measurements using F-tests on a distribution grid model

Anomaly detection in measurements within electrical distribution grids is essential for maintaining accurate state estimation and ensuring efficient grid management.This paper presents a novel anomaly detection framework that extends a linear state estimation model with statistical -tests to identify deviations in distributed voltage or current phasor measurements.Assuming knowledge of the grid topology and correct phasor measurements, but subject to zero-mean complex Gaussian noise with known standard deviations, the anomaly detection approach checks whether deviations in measured data exceed normal fluctuations.The proposed algorithm flags potentially anomalous phasor measurements when the corresponding -value from an -test falls below a predefined significance level.Extensive simulations on a medium-voltage grid demonstrate the framework's ability to detect subtle magnitude and angle anomalies across a wide range of operating scenarios, maintaining robust performance even under reduced measurement coverage.The approach is scalable, interpretable, and well-suited for deployment in resource-constrained monitoring and data quality assurance within electrical distribution grids.