A Deep Learning and Inertia-Aware Load Shedding Framework for Mitigating Load-Altering Attacks

The widespread integration of information and communication technologies into modern power systems has increased their vulnerability to cyber-physical threats, such as load-altering attacks (LAA). These attacks can cause rapid load changes, potentially triggering protective mechanisms like under-frequency load shedding (UFLS). Existing approaches for mitigating these attacks are limited, and they mostly rely on preventive measures or neglect system dynamics. In this paper, we propose a novel online framework for the detection and mitigation of LAAs that addresses these limitations. The detection component employs a convolutional neural network–long short-term memory autoencoder (CNN-LSTM AE) architecture to capture anomalies in load consumption data. For mitigation, we propose an inertia-aware load shedding scheme that dynamically adjusts the shedding amount based on the real-time frequency and the magnitude of the attack. This approach prevents overshedding caused by predefined UFLS relay settings and mitigates undershedding by considering the system’s real-time inertia. To this end, a variable forgetting factor recursive least squares (VFF-RLS) algorithm is proposed, which can track inertia variations within a few seconds. The proposed framework is compatible with both synchronous generator-based and converter-interfaced generator-dominated grids. Simulations indicate the effectiveness of the proposed framework in maintaining frequency stability under a wide range of attack scenarios.