Online Learning-based Islanding Detection Scheme for Grid-Connected Systems

Data aggregation in smart grids is a key component for emergency responses during abnormalities in the grid. To efficiently utilize the aggregated data, and achieve fast identification of these abnormalities, this paper develops an online islanding detection approach. The development of the technique is realized with an online learning algorithm implemented using the large-scale support vector machine (LaSVM). The algorithm adopts a classification problem for islanding detection in grid-connected systems by considering a set of independent variables and unknown variables. The independent variables are related to the known islanding events in the grid-connected system, and the unknown variables are related to the dynamics of the grid operating in real-time. The proposed approach solves this problem by training the known and unknown variables and identifying new instances through sequential minimal optimization. The training and validation results provided indicate 99.8 % accuracy for islanding detection under standard operating conditions of the grid-connected system.