Due to the intrinsic complexity of real-world power distribution lines, which are highly non-linear and time-varying systems, modeling and predicting a general fault instance is a very challenging task. Power outages can be experienced as a consequence of a multitude of causes, such as damage of some physical components or grid overloads. Smart grids are equipped with sensors that enable continuous monitoring of the grid status, hence allowing the realization of control systems related to different optimization tasks, which can be effectively faced by Computational Intelligence techniques. This paper deals with the problem of faults modeling and recognition in a real-world smart grid, located in the city of Rome, Italy. It is proposed a suitable classication system able to recognize faults on medium voltage feeders. Due to the nature of the available data, the one-class classication framework is adopted. Experiments are presented and discussed considering a three-year period of measure
Fault recognition in smart grids by a one-class classification approach / DE SANTIS, Enrico; Livi, Lorenzo; FRATTALE MASCIOLI, Fabio Massimo; Alireza, Sadeghian; Rizzi, Antonello. - STAMPA. - (2014), pp. 1949-1956. (Intervento presentato al convegno IJCNN 2014 - International Joint Conference on Neural Networks tenutosi a Beijing; China nel July 6-11, 2014) [10.1109/ijcnn.2014.6889668].
Fault recognition in smart grids by a one-class classification approach
DE SANTIS, ENRICO;LIVI, LORENZO;FRATTALE MASCIOLI, Fabio Massimo;RIZZI, Antonello
2014
Abstract
Due to the intrinsic complexity of real-world power distribution lines, which are highly non-linear and time-varying systems, modeling and predicting a general fault instance is a very challenging task. Power outages can be experienced as a consequence of a multitude of causes, such as damage of some physical components or grid overloads. Smart grids are equipped with sensors that enable continuous monitoring of the grid status, hence allowing the realization of control systems related to different optimization tasks, which can be effectively faced by Computational Intelligence techniques. This paper deals with the problem of faults modeling and recognition in a real-world smart grid, located in the city of Rome, Italy. It is proposed a suitable classication system able to recognize faults on medium voltage feeders. Due to the nature of the available data, the one-class classication framework is adopted. Experiments are presented and discussed considering a three-year period of measureI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
