Smart Detection of Voltage Dips Using Voltage Harmonics Footprint

In order to enable voltage ride-through operation, fast and reliable voltage dips detection is necessary. It has been noticed by the analysis of a huge number of measurement results recorded in-field, in laboratory, and obtained by simulation that the voltage dips may be characterized by specific low-order voltage harmonics pattern, which appears at the beginning and at the end of the voltage dips. This feature is named harmonic footprint. This paper proposes a new method of voltage dips detection based on harmonic footprint as part of smart algorithm for detection and classification of power quality events. The aim is to achieve reliable smart voltage dips detection in 1 ms range. Several methods for harmonic estimation in real time were compared in order to choose the optimal one. The footprint is modeled by mathematical function and applied for voltage dips smart detection using recurrent neural network. The proposed method was tested for voltage dips detection analyzing signals from measurement in the real distribution grid (680 records), computer simulations using IEEE-13 bus test grid, and testing in the laboratory. The reliability (detection rate) and speed of voltage dips detection are tested and verified. Furthermore, the future research directions for presented method are outlined.