Load-altering attack detection on smart grid using functional observers

Smart grids are becoming more common due to their capacity to accommodate secondary sources, like green energies from solar panels or wind farms. However, the attack surface also grows with more equipment in the network, making it necessary to secure appropriately. With more sensors distributed on the network, it becomes easier for an attacker to hack into one and send false information to the central to destabilize the power generation and distribution. Load-altering attacks do precisely that and have a destructive potential since the generator reaction can cause network instability. Traditional techniques, like those based on the Kalman filter, for example, may pose numerical issues due to the large size and sparsity of the system matrices, failing to provide good results or wasting computational resources. We propose an LMI-based approach to design a bank of residual generators for functional observers to detect such attacks. This approach has the advantage of using a reduced order arbitrary dynamic system, making it suitable for large-scale smart grids, and the use of LMI, allowing the easy insertion of restrictions.