Novel Consumer Power Event-Driven Methods for Remote Estimation of Smart Meter Error

Remote in-service detection of energy metering anomalies and identification of faulty meters remains an active challenge due to the limitations of existing methods. Traditional energy conservation-based techniques require readings from all consumers’ meters and are not applicable in the case of partial deployment of smart meters in the distribution grid. This paper presents two novel methods for remote estimation of active power measurement error of individual smart energy meter that utilizes consumer-side power events. The first approach, Inverse Problem Solution (IPS), utilizes feedforward neural network model trained under reference conditions for the prediction of the expected power change at the location of the sum meter. The power measurement gain error of the consumer meter is estimated by minimizing the difference between the predicted power change and the power change measured by the sum meter. The second approach, Electrical Data Augmentation (EDA), utilizes feature engineering with injection of different levels of gain error of meter power measurement to create dataset for the training of a random forest regression model dedicated to predict the gain error directly. Both techniques were examined using synthetic datasets generated from power flow simulation of low-voltage distribution grid. It is shown that both methods achieve sub-1% root mean square error (RMSE) of estimation of power measurement gain error, with EDA demonstrating slightly superior performance in terms of meter error prediction RMSE and robustness to grid technical losses variations.