The immune system of the human body follows a process that is adaptive and learns via experience. Some algorithms are designed to take advantage of this process to determine solutions for complex problem domains. The collection of these algorithms is known as Artificial Immune Systems. Among this collection, one important algorithm is "The Danger Theory." In this thesis, an application of the algorithm has been implemented to solve an electrical grid problem. This problem of interest is the automatic detection of faulty and failure conditions in the electrical grid. A novel application of the Artificial Immune System algorithm is presented to solve this problem (i.e., to find faults in electrical-grid data in an automated fashion). The methodology treats streams of electrical-grid data as artificial antigens, and uses artificial antibodies to identify and locate potentially harmful conditions in the grid. The results demonstrate that the approach is promising. I believe this approach has a good contribution for the emerging field of Smart Grids.