ENHANCING POWER SYSTEM SECURITY THROUGH RESILIENT FREQUENCY REGULATION IN THE FACE OF HYBRID CYBER-ATTACKS

Abstract

The growing reliance on modern communication technologies in power systems for Frequency Regulation (FR) introduces vulnerabilities to cyberattacks, posing significant threats to system stability and reliability. These attacks can disrupt the coordination among various components, such as sensors, control centers, and actuators, thereby compromising the integrity of FR analysis. In response, this article proposes a resilient solution in the form of a deep-learning-based Attack Detection and Mitigation system. By integrating advanced AI techniques, this system aims to fortify the security of FR operations within the cyber-physical framework, swiftly identifying and neutralizing cyber threats. Ultimately, this approach ensures the continuous and reliable operation of power systems, mitigating the risks posed by hybrid cyberattacks and safeguarding critical infrastructure. The proposed system represents a proactive approach to mitigating the escalating risks associated with cyberattacks targeting FR in power systems. Through its deep-learning algorithms, the system can dynamically adapt to emerging threats, enhancing the resilience of FR analysis against malicious intrusions. By bolstering security measures within the cyber-physical model, the system minimizes the potential impact of cyberattacks on power system stability and reliability. Moreover, its ability to detect and mitigate threats in real-time ensures uninterrupted operation, thereby safeguarding the functionality of power systems even amidst the evolving landscape of hybrid cyber threats. This resilient solution represents a crucial step towards fortifying power system security and maintaining essential services in the face of adversarial cyber activities. Keywords: Attack detection and mitigation (ADM) system, Frequency Regulation(FR), hybrid power system, Renewable Energy Sources (RESs), resiliency.