# Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
## 7 Recommendations
AI will be essential for optimizing WAMS functionality, particularly in fault detection, predictive maintenance, and grid resilience. The application of deep learning, reinforcement learning, and quantum computing will enable more accurate fault localization, reduced system downtime, and improved operational efficiency. By leveraging AI-enhanced WAMS, developing economies can optimize power flow dynamics, reduce transmission losses, and enhance the overall adaptability of their power grids. Finally, policy standardization and regulatory frameworks must be established to ensure the interoperability and long-term sustainability of WAMS deployment. The absence of global WAMS implementation standards has resulted in fragmented adoption and compatibility issues across different grid infrastructures. Future efforts should prioritize the development of interoperability guidelines, cybersecurity compliance frameworks, and international research collaborations to ensure harmonized WAMS deployment across power networks. Future Work  
Although significant research has explored WAMS applications in modernized power systems, several gaps remain regarding its adaptation for traditional power grids, particularly in developing economies. Future research should focus on refining WAMS models that can be tailored to aging power infrastructures, improving fault detection mechanisms, and developing data-driven methodologies for optimizing grid expansion planning. This is especially crucial in Nigeria and South Africa, where frequent grid collapses and unreliable energy supply have hindered economic growth and industrial development. A key area for future research is the expansion of WAMS to support renewable energy microgrid development. Many developing economies rely on off-grid and microgrid solutions to address energy access challenges [75; 76]. Future studies should investigate how WAMS-enabled microgrid expansion can improve electrification in remote areas, ensuring grid stability while supporting decentralized renewable energy systems. Additionally, the WAMS-based optimization of distributed energy storage could play a crucial role in mitigating power supply fluctuations and enhancing overall system reliability. Another pressing research avenue is the integration of AI-driven cybersecurity frameworks into WAMS-based power networks. With increasing cyber threats targeting critical power infrastructures, future work should explore adaptive cyber-physical security models that leverage machine learning and multi-agent systems to detect, mitigate, and prevent attacks in real time. Additionally, the development of self-learning cybersecurity architectures that can dynamically adjust to evolving threats will be instrumental in ensuring grid security in CPPS environments. Furthermore, enhanced protections against phishing attacks in PMU-based systems should be explored, as these attacks continue to pose a growing risk to data integrity and network security. SGAM will also play a key role in the future development of WAMS. Future studies should establish methodologies for integrating WAMS within the SGAM's five-layer framework, enhancing grid interoperability, observability, and efficiency. Identifying scaling challenges and proposing solutions for implementing SGAM in traditional power grids, particularly in sub-Saharan Africa, will be crucial for modernizing outdated grid infrastructure. Finally, economic and policy research should be expanded to support large-scale WAMS deployment. Future studies should analyze financing models for widespread WAMS adoption, including opportunities for international investments in smart grid infrastructure. Additionally, investigating the long-term economic benefits of WAMS deployment, particularly in relation to grid stability, renewable energy penetration, and industrial growth, will provide crucial insights for policymakers and energy stakeholders. Conceptualization, C.E.O. and S.O.O. ; methodology, C.E.O. ; software, C.E.O. and J.T. ; validation, C.E.O., J.T. and S.O.O. ; formal analysis, C.E.O.