Fault recognition in smart grids by a one-class classification approach

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 measurements of fault events gathered by ACEA Distribuzione S.p.A., the company that manages the smart grid system under analysis. Results demonstrate the effectiveness and validity of our approach.