Strategic Behavior Analysis and Modeling for Resilient Resource Allocation

In an era where power systems face increased cyber threats,social media data,especially public sentiment during outages,emerges as a crucial component for devising defense strategies.We present a methodology that integrates sentiment analysis of social media data with advanced reinforcement learning techniques to tackle uncertain load redistribution cyberattacks.This approach first employs VADER and Support Vector Machine(SVM)sentiment analysis on collected social media data,revealing insightful information about power outages and public sentiment.Proximal Policy Optimization(PPO),a state-of-the-art reinforcement learning method,is then applied in the second stage to leverage these insights,manage outage uncertainty,and optimize defense strategies.The efficacy of this methodology is demonstrated on a modified IEEE 6-bus system.The results underscore our approach's effectiveness in utilizing social media data for a nuanced,targeted response to cyberattacks.This pioneering methodology offers a promising direction for enhancing power grid resilience against cyberattacks and natural disasters,highlighting the value of social media sentiment analysis in power systems security.