Evaluating Fuzzy Machine Learning for Smart Grid Instability Detection

Smart grid is an advanced concept of power systems devoted to harmonize electricity and communication in system networks. It is able to provide real-time information to producers, operators and consumers. There is an urgent demand to efficiently route supplied energy to consumer domains such as for instance, households, organisations, industries, and also smart cities. In this context, a smart grid with a stable system is required to supply the dynamic energy demand. In this paper, we propose a method aimed to detect whether a smart grid is in an unstable state. For this task, we consider fuzzy supervised machine learning. We evaluate a dataset composed by 60000 smart grid observations and we obtain interesting results, by demonstrating the effectiveness of a fuzzy machine learning model for the detection of smart grid states.