Identifying Concept Drift with Supervised Algorithms in Smart Grids
Ayşe Sayın, Mostafa Mohammadpourfard, Mehmet Tahir Sandıkkaya · 2023
Early and correct disclosure of anomality is critical for realiable and secure activity of the smart grid. Machine learning based outlier detection schemes could be helpful when there is no concept drift in the smart grid. Concept drift is considered an unforeseeable change in the distribution of the data in machine learning [4]. In this study, an anomaly detection is formulated as a machine learning problem and a robust detection algorithm is proposed. Numerical results demonstrate effectiveness of the proposed solution to detect anomaly and contingency besides integrity attacks in the smart grid.