Online stability assessment for isolated microgrid via LASSO based neural network algorithm - Ahmed Lasheen; Hatem F. Sindi; Hatem H. Zeineldin; Mohammed Y. Morgan (Energy Conversion and Management: X)
Online prediction of the dominant modes is very important for microgrid operation. The dominant modes determine microgrid stability and the active and reactive power oscillations. Therefore, online prediction of these modes is essential to check the microgrid stability periodically. Consequently, this paper introduces an artificial intelligent algorithm to identify the dominant modes of the microgrid. This algorithm combines a cascaded feedforward neural network with the least absolute shrinkage and select operator (LASSO). The LASSO algorithm is used to extract the most important data that affects the dominant modes. On the other hand, the cascaded feedforward neural network is trained using LASSO data to identify the microgrid dominant modes. The proposed algorithm is tested using a 6-bus AC microgrid. The results show that the proposed algorithm significantly determines the dominant modes of the microgrid by using a minimum set of data determined by LASSO.