Enhancing power grid reliability: a hybrid blockchain and machine learning approach

Abstract

As contemporary power grids are becoming more complex with the integration of renewable energy sources, distributed generation, and smart grid technologies. Conventional contingency analysis techniques, based on centralized architectures and static rule-based evaluations, tend to be inadequate in real-time fault detection, automated response, and cybersecurity. This paper suggests a hybrid approach that combines machine learning algorithms with blockchain technology to improve both predictive intelligence and security of contingency analysis. For the IEEE 30-bus test case, different line outage and generator failure cases were simulated. Different machine learning models, such as random forest (RF), support vector machine (SVM), and gradient boosting (GB), were trained to classify and predict these contingencies. In parallel, cryptographic primitives like advanced encryption standard (AES), Rivest–Shamir–Adleman (RSA), and elliptic curve cryptography (ECC) were tested in a blockchain setting to provide security for event data and enable automatic recovery steps through smart contracts. Outcomes illustrate that the GB showed the maximum fault classification rate (93.4%), and ECC ensured light yet robust data protection for blockchain activities. Against the conventional system, the designed model enhanced the response time in case of faults, accuracy, and system fault tolerance. This two-layer mechanism presents a scalable, proactive, and cyber-safe mechanism for the power grid in the future.