# Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
## 4 WAMS and Artificial Intelligence  
As discussed above, WAMS addresses the fundamental issue of lacking situational awareness in a conventional SCADA infrastructure. Especially in a widely independent-interdependent energy infrastructure, the WAMS architecture can make the grid situationally aware and the conventional grid smarter without the need to overhaul a significant part of the design. In terms of complexity, the PMU, PDC, synchrophasor, and communications infrastructure technologies have seen different obstacles being presented that mitigate the deployment of the WAMS architecture on a grid [24]. As pointed out during the analysis of different communication infrastructures, the various algorithms needed for their deployment are as complex as WAMS [1,42]. In every engineering endeavor, the global optimum operation of every design is paramount, especially in terms of efficiency and cost. In the case of WAMS, [PERSON]'s algorithm is used, and an N-1 contingency may arise from the binary imperialistic competition algorithm. This must be duly calculated and analyzed [1,42]. The research in [42] also showed that communication infrastructure design can conform to the efficient PMU design and thus become more cost-effective. In practice, these deployments have been known to change based on load differences and load potentials developing across the different load centers on the grid [8,24]. AI, therefore, becomes necessary to conduct these complex calculations and algorithms to ensure a proper and effective means of deploying WAMS along the grid with the least number of PMUs to achieve the maximum situational awareness of the grid. The Dijkstra algorithm could be modified at any time with an AI infrastructure by considering other factors like fault lines and vulnerable sections in optimizing PMU node placement [42]. With AI technology, the effective means of maximizing the communication infrastructure becomes feasible and cost-effective. Once this is considered, multiple studies have shown that WAMS adoption would increase drastically across economies that lack the financial incentives to deploy a smart grid [1,5,11,44]. In the race to deploy renewable energy systems in transitioning to a green economy, especially in largely sunny sub-Saharan Africa, the deployment of solar generation is paramount [57]. The intermittency of renewable energy sources remains a significant challenge that must be addressed to prevent major blackouts and ensure a reliable power supply. This can be achieved through strategies such as minimizing energy losses and improving consumption efficiency [52]. WAMS, when integrated with AI, offer a transformative solution by enabling the real-time determination of the safest and most efficient single-PMU paths for energy transmission across the grid [58,59]. This integration leverages AI's advanced computational capabilities to enhance the speed, accuracy, and reliability of grid monitoring and fault management. AI significantly enhances the functionality of WAMS by refining metaheuristic optimization algorithms, including immune genetic algorithms, search trees, and simulatedannealing, while simultaneously ensuring the robustness and integrity of the WAMS architecture [60; 61]. These optimization techniques are critical in addressing fault detection, stability assessment, and system reliability, particularly in complex and evolving power grids. [PERSON] et al. identified several metaheuristic-based algorithms that form the backbone of AI deployment in power system networks. Their evaluation of four primary categories of optimization techniques--artificial neural networks (ANNs), fuzzy logic, evolutionary methods, and expert systems--highlighted the unique advantages of evolutionary-based algorithms, particularly those leveraging swarm intelligence, for power system optimization and fault detection [62; 63].