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Abstract Enhancing the resilience and reliability of power grids is crucial amid rising cyber threats and system complexities. To address these challenges, this paper proposes an energy‐efficient, consortium blockchain‐based global alarm system for power grid management. Using smart contracts and the proof of‐authority consensus algorithm, the alarm system triggers global alarms upon detecting local anomalies, ensuring a prompt response to partition the power grid and mitigate failures. The effectiveness is validated by simulating the Iberian power system with 15 providers from various regions. Key metrics, such as load shedding, damage reduction, energy consumption, latency, and transaction costs, are used to assess the performance. Through simulations, we show that the blockchain‐based system effectively limits the damage propagation and the load shedding during cascading failures by delaying the onset of instability and maintaining lower damage levels compared to non‐blockchain scenarios. Our investigations reveal that the proposed global alarm mechanism reduces the damage and load shedding by up to 29% and 87%, respectively, showcasing its potential for preventing widespread outages. 

The digital transformation of power system introduces False Data Injection Attacks (FDIAs) on voltage stability that compromises the operational integrity of power grids. Existing detection mechanisms for FDIAs often fall short as they overlook the complexities of cyberattacks targeting voltage stability and rely on outdated models that do not capture the dynamic interplay between power system operations and potential threats. In response to these gaps, this paper proposes a novel FDIA detection method designed specifically for voltage regulation vulnerabilities, aiming to enhance the voltage stability index. The proposed method utilizes an unsupervised learning framework capable of identifying cyberattacks targeting voltage regulation. A bi-level optimization approach is put forward to concurrently optimize the objectives of both attackers and defenders in the context of voltage regulation. The effectiveness of this approach is validated through comprehensive training and testing on a variety of attack scenarios, demonstrating superior generalization across different conditions. Extensive simulations on the Iberian power system topology, with 486 buses, show that the proposed model achieves more than 93% detection rate. These results highlight the robustness and efficacy of the proposed strategy in strengthening the cyber resilience of power systems against sophisticated FDIA threats on voltage stability.