# Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
## 4 WAMS and Artificial Intelligence
Evolutionary algorithms offer self-adaptive mechanisms, global search capabilities, and robust convergence properties, making them well suited for dynamic power system environments where real-time decision making is essential. Among these, particle swarm optimization (PSO) has been widely recognized for its effectiveness in optimizing grid stability, designing protection schemes, and diagnosing faults [46; 62; 64]. PSO operates by mimicking the social behavior of particles (or agents) in a multidimensional space, allowing for the efficient exploration of search spaces and the identification of optimal solutions in power system applications. This technique has been particularly effective in enhancing fault detection precision, reducing response times, and improving grid resiliency. [PERSON] et al. further emphasized the application of AI-driven optimization in WAMS-based systems by exploring hybrid optimization methods for fault detection [65]. Their research introduced a hybrid approach combining PSO techniques with [PERSON]'s method, comparing it against traditional linearization techniques that have historically dominated the field. By leveraging PSO's global optimization capabilities alongside [PERSON]'s rapid convergence properties, their approach significantly improved fault localization accuracy, reduced computational complexity, and enhanced the overall adaptability of WAMS in dynamic grid environments [65]. These findings illustrate the growing role of AI-driven optimization in modern power system monitoring, demonstrating AI's potential to revolutionize fault management, stability enhancement, and operational efficiency in smart grid architectures. Despite their consistent use, the PSO algorithm still suffers from several limitations. PSO has limitations in handling fast-changing voltage and current parameters, which are common in dynamic grid conditions. On the other hand, ant colony optimization (ACO) and Bee Colony Optimization (BCO) overcome these limitations by offering adaptive protection schemes capable of responding to real-time changes in the grid. This adaptability makes ACO particularly suitable for ensuring grid stability under rapidly fluctuating conditions [62]. These advanced optimization techniques also provide a foundation for training AI models to detect and classify new fault patterns. AI systems, equipped with these models, can rapidly deploy adaptive protection schemes and reconfigure the grid in response to evolving conditions, thereby ensuring overall grid stability. The continuous learning capability of AI allows it to evolve alongside the grid, improving resilience and performance over time. Furthermore, the integration of AI into WAMS has broader implications for the future of power systems. AI can serve as the cornerstone for a flexible AC transmission system (FACTS), enabling precise control of power flows and improving grid flexibility. It is also central to smart grid development, facilitating efficient energy management, demand response, and the integration of distributed energy resources. In modern CPPS, AI's ability to process vast amounts of data in real time ensures enhanced situational awareness, fault detection, and mitigation. AI's role in WAMS technology extends beyond simple optimization. It provides a comprehensive framework for enhancing grid stability, reliability, and adaptability in the face of increasing complexity and variability in modern energy systems. This synergy between AI and WAMS paves the way for a more resilient, efficient, and sustainable power grid capable of meeting the demands of renewable energy integration and evolving load dynamics. Optimization problems in WAMS can be addressed for the simultaneous modification and application of WAMS in degenerate power system networks [61]. Advanced communication infrastructure using fiber optics technology can also be flexibly employed in the WAMS architecture to achieve more cyber protection nominally guaranteed with GPS [25].