Design of adaptive AI algorithms for power system security protection

The security protection of current power systems faces severe challenges posed by increasingly complex cyberattacks and diverse security threats. To enhance system security capabilities, this paper designs an adaptive security protection method based on deep learning. At the theoretical design level, a three-layer architecture is proposed: the perception layer handles data collection and preprocessing, the analysis layer performs situation assessment and predictive early warning, and the decision layer generates dynamic protection strategies. At the implementation mechanism level, specific protection methods based on this architecture are elaborated in detail. Security incident prediction is achieved through an enhanced LSTM network, dynamic protection strategies are generated using deep reinforcement learning, and a multiscenario collaborative decision-making mechanism completes the overall system protection. Experimental validation demonstrates that this solution achieves a detection rate of 98.2% for known attacks and over 82.5% for variant attacks in practical power grid applications, reducing system security incidents by 82.3%.