Magnitude of Voltage Sags Prediction Based on the Harmonic Footprint for Application in DG Control System

Grid condition information extraction is very important for converter control systems in order to perform special functionalities, especially grid support during low voltage ride through (LVRT). As a part of grid condition detection, the voltage sag detection provides information about the start of a fault and obtains indices that determine the behavior of the whole control system of the converter during a fault. Estimation of the magnitude of voltage sag (MoVS) is essential for obtaining proper grid support. This paper presents an algorithm for MoVS (as defined in IEEE Std 1564-2014) prediction. The existence of correlation between magnitudes of a set of low-order harmonics during the transient of voltage PQ events with MoVS is for the first time determined and statistically proved. Correlation is mathematically formulated and the prediction function is obtained on the basis of real grid measurements. Complete algorithm for magnitude prediction is explained, and its possible application in distributed generation control is presented. It is shown that significant time gain is achieved, which may be used as an additional benefit in LVRT operation.