Methodology Based on Adaboost Algorithm Combined with Neural Network for the Location of Voltage Sag Disturbance

The correct location of the source of voltage sags is not a trivial task due to the short duration of these events and their rapid propagation in the distribution feeder. This paper proposes a method based on an ensemble method of the type Adaboost, with neural networks as base classifiers to determine the area where the voltage sag source is located. A voltage sag at a bus affects all other feeders, i.e. this disturbance is propagated in the whole system. The data management from smart meters installed in distribution feeders and decision support tools can become a viable alternative. In this sense, the smart meters could extract feature of the voltage sag and send it to the utility. At the utility, the AdaBoost performs location of the region by measuring the input’s features similarity to samples from the training set. For this purpose, it was necessary to analyze the relevance of each feature extracted from smart meters’ voltage signals to establish the structure which best represents the propagation of the disturbance in the system. The AdaBoost with Neural Network was tested in different scenarios of the 13-bus IEEE test feeder and was able to estimate the region with a good accuracy.