A time‐stamp calibration method for smart meter data based on interpolated least squares

Abstract

Temporal asynchrony in smart meter data, caused by clock drift and protocol failures, significantly undermines grid state estimation and fault location. Traditional synchronization methods often rely on costly hardware or real‐time protocols and are ineffective for historical data and legacy devices. This paper proposes a post‐processing method that estimates and corrects time offsets in historical smart meter data using only electrical measurements, thereby eliminating dependence on external time sources. Cubic spline interpolation is first applied to refine sub‐meter data granularity, enhancing sensitivity to small timing errors. An interpolated temporal model is then constructed based on energy conservation, with network losses incorporated via a clustering technique applied to squared current data. A generalized damped recursive least squares (GDRLS) algorithm enables online estimation of sub‐meter time error ratios. Validation on an actual transformer area demonstrates high synchronization accuracy and low mean absolute error. Interpolation reduces timing error substantially, and GDRLS significantly improves accuracy compared to limited‐memory recursive least squares. The method exhibits strong robustness under measurement noise and achieves high reliability when the sub‐meter energy contribution exceeds a low threshold. The proposed approach offers a novel, low‐cost solution for time‐stamp post‐calibration, strengthening the data foundation for grid monitoring and advanced analytics.