# Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
## 5 Cyber-Physical Power Systems and Smart Grid Development
In each instance of the graphical, mechanical, probabilistic, and simulation models, they identify the Petri net model as the only one of the graphical models capable of visualizing sCPPS models to define both the interaction phenomena between the continuous nature of conventional power systems and emphasize the dynamic nature of cyber systems integrated with CPPS [71]. This emphasis is needed if accurate simulations provide the assessment of possible cyber threats to potential WAMS-based models. Similarly, research studies by [PERSON] et al. followed a similar position, suggesting that potential CPPS may also include cloud-based energy management systems. They argue that financially motivated attacks will therefore seek to compromise the energy systems, since cloud-based systems have the potential to reduce financial losses in smart grid systems [67]. Aside from these attacks, coordinated cyber-physical attacks are potential ways that CPPS could be compromised and infiltrated [51; 73]. If WAMS are to be deployed in conventional power system networks to transform them into credible CPPS, optimization models for deploying PMUs on the smart grid networks are to be prioritized since most dynamic load-altering attacks exploit PMU placement on WAMS [51; 67; 71]. Mitigation strategies can include several layers of optimization models, including multilayer models that ensure that the different categories of cyber-physical attacks on PMUs, RTUs, and SCADA systems can be identified and accurately addressed to ensure overall CPPS integrity [53; 68; 69]. State estimation models, the foundation of most energy management systems, can be compromised using frequency transmission techniques like false data injection attacks targeting simple RTU-PMU placement on the lines, which can ensure isolation at critical physical and cyber conditions that can mitigate the collapse of erstwhile conventional systems and current smart grid architecture [67; 71]. The asynchronous, dynamic, and concurrent nature of CPPS in the operation means that PMU and WAWS operation will both be dynamic and designed to be responsive, especially in cloud-based systems; therefore, in such cases,state estimation may be performed spontaneously for other dynamic assessments to be conducted [67; 68; 71]. In this scenario, a multi-level optimization could make the system respond to dynamic changes in transient systems, which are mostly avenues for compromising smart grid systems. In simple terms, a multi-level CPPS attack will require a multi-level optimized mitigation technique to address potential damage to both system integrity and financial models of CPPS [67]. These complex optimization techniques can be operated well using AI systems integrated into CPPS and smart grid architecture. In such AI-integrated smart grid systems, machine learning (ML) models can be designed to test for the Karush-Kuhn-Tucker (KKT) conditions, which are essential in processing the nonlinear programming data reported from the dynamic system operations [67; 68].