Modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one-class classification

Detecting faults in electrical power grids is of paramount importance, either from the electricity operator and consumer viewpoints. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical condition of the elements forming the whole infrastructure (e.g., cables and related insulation, voltages and currents, breakers status and so on). In real-world smart grid systems, usually, the operator collects additional information that are indirectly connected to the operating status of the grid itself, such as meteorological information. Designing a suitable recognition (discrimination) model of faults in a real-world smart grid system is hence a challenging task. This follows from the heterogeneity of the information that actually determine a typical fault condition. The second point is that, for synthesizing a recognition model, in practice only the conditions of observed faults are usually meaningful. Therefore, a suitable recognition model should be synthesized by making use of the observed fault conditions only. In this paper, we deal with the problem of modeling and recognizing faults in a real-world smart grid system, which supplies the entire city of Rome, Italy. Recognition of faults is addressed by following a combined approach of multiple dissimilarity measures and one-class classification techniques. We provide here an in-depth study related to the available data and the correlations with respect to the solutions found by the proposed one-class classifier. We offer also a comprehensive analysis of the fault recognition results by exploiting a fuzzy set based mechanism for the decisions of the classifier.