# Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
## 6 Conclusions  
SCADA systems have become an integral part of conventional power systems. Emerging economies including Nigeria and other African countries have adopted SCADA systems to integrate their multiple protection components, including current transformers, power transformers, and relays [74]. The SCADA systems in these countries promise more sensitivity and awareness. Still, the incessant reports of faults on the grid have made them mostly incapable of participating in recent conversations around clean energy and renewable energy technologies. WAMS using PMUs, RTUs, and, in some cases, some SCADA infrastructures have been able to fill the gap of power system development and expansion comprehensively. The rise of AI has sparked expert conversations about the evolution of WAMS to accommodate cybersecurity challenges that may compromise clean energy development and renewable energy integration into the grid. With the increase in energy consumption due to population and the high impact of climate change (heat wave), power system expansion and potential CPPS are required to meet this demand [72]. This demand can greatly compromise existing energy infrastructure, especially conventional power system infrastructure in Nigeria and sub-Saharan African countries. Most of these energy grid systems are marred by inflexibility making them unresponsive to dynamic changes occurring in the grid. While smart grid systems have been installed in most of these cases to integrate DERs into the grid, CPPS designs remain the feasible solution for the overall grid [68]. Environmental factors compromise some aged systems, making them prone to faults and grid collapse during energy crises. Energy experts and researchers have identified WAMS as the significant way of making these conventional systems into smart grids and potentially transforming them into cyber-physical power system networks operating via optical-fiber-powered systems. This revolution in energy configuration will become the needed factor in placing these developing-world economies to contribute and participate in modern renewable energy conversations. However, WAMS are known to be expensive, and their flexibility depends on the design model applied in distributing the PMUs across the power system infrastructure. AI can play a significant role in ensuring that the WAM infrastructure is distributed along the line in a way that is both affordable and operationally effective. However, the CPPS and smart grid systems may also become prone to cyber-physical attacks, which could heavily compromise power system operation. Optimization techniques are the safest approach to deploy PMUs on the grid cost-effectively and to improve the integrity of the overall systems. These issues of optimization are quite concerning since the data collected are numerous and dynamic. The dynamic nature of the CPPS and smart grids will mean that highly accurate stochastic analysis will be required to identify cyber threats and still carry out control functions. AI could be efficient in such roles: rapidly retrieving and processing data, flagging cyber threats, and stacking up CPPS and smart grid behaviors simultaneously. AI is already informing power system expansion, the growth of the smart grid, and algorithms to maximize energy efficiency [34; 42]. The observability and controllability of the power system network will also be greatly increased since AI models can improve the collection and analysis of the PMU data along the line and provide recommendations to control system operators for decisive steps to ensure power system integrity. AI could rapidly respond to security breaches and shut down sensitive infrastructure that may compromise long-term operations. In conclusion, integrating AI with WAMS offers transformative improvement in fault mitigation. While traditional SCADA systems have long served as the backbone for monitoring and controlling electrical grids, their limitations in real-time data processing and prediction are increasingly evident.