A Method for Assessment of Power Consumption Change in Distribution Grid Branch After Consumer Load Change

This research targets prediction of power consumption change (PCC) in the branch of electrical distribution grid between a sum meter and consumer meter in response to consumer load change. The problem is relevant for power preservation law-based event-driven methods aiming for detection of anomalies like meter errors, electricity thefts, etc. The PCC in the branch is due to the change of technical (wiring) losses as well as change of power consumption of loads connected to the same distribution branch. Using synthesized dataset set a data-driven model is built to predict PCC in the branch. Model performance is assessed using root mean squared error (RMSE), mean absolute, and mean relative error, together with their standard deviations. The preliminary experimental verification using a test bed confirmed the potential of the method. The accuracy of the PCC in the branch prediction is influenced by the systematic error of the meters. Therefore, the error of the consumer meter and the PCC in the branch cannot be evaluated separately. It was observed that the absolute error of the estimate of power measurement gain error was observed to be within ±0.3% and the relative error of PCC in the branch prediction was within ±10%.