## 4 Applications of AI Techniques in Power Systems  
AI tools can effectively solve power system problems when the characteristics of the problem align well with those of the AI tool. It is worth noting that the nonlinear behavior of various components and the entire system is an essential aspect of power system problems that are often relevant to AI applications. Nonlinearities in power systems can be classified into three categories: near linear, continuous nonlinear, and discrete. Many power system problems can be treated as near linear during regular operation, allowing for numerical approaches to minimize solution times. As power systems experience increased stress due to larger loads and power transfers, the nonlinearities may become too significant to ignore, and control limits may become necessary [68]. Both near-linear and nonlinear power systems can also experience planned and unplanned discrete changes due to switching operations, which can be automatic or manual. The nonlinear behavior of components in power systems can make it challenging to address each component individually. However, at the same time, nonlinear problems are well suited for AI tools to tackle. Regarding the application of artificial intelligence (AI) in power systems as case studies, real-world applications demonstrate the practicality of AI technologies and highlight their successes and limitations. For instance, a case study involving a major power utility's use of machine learning algorithms to predict and manage load distribution could be detailed, illustrating the positive impact on operational efficiency and energy savings. Conversely, a discussion of a situation where AI failed to predict a system failure due to unforeseen circumstances, such as extreme weather conditions, would provide valuable lessons on the limitations of current AI implementations. Including such examples would offer readers a clearer understanding of AI technologies' practical applications, challenges, and real-world performance in managing complex power systems. These enhancements will ensure the discussion is theoretical and grounded in practical evidence reflecting AI's current state and potential in this field. AI tools can be used to train personnel to solve problems in various areas of power systems. Experience and good heuristics often improve the quality and efficiency of solutions. Power system personnel can participate in simulations that utilize AI tools to apply stored knowledge and gain experience and insights from others. To determine which characteristics of power system problems are suitable for AI tools, the problems can be categorized based on power system operation, restoration, power system security, power system stability and stabilizer, voltage stability, and protection. Table 4 presents AI's critical roles in various aspects of power system stability, control, and protection, highlighting the AI techniques used and the benefits they bring to the power system. \begin{table}
\begin{tabular}{c c c c c} \hline
**AI Techniques** & **Role in Stability** & **Role in Protection** & **Role in Control** & **Other Roles** \\ \hline \multirow{3}{*}{Machine Learning} & Predicting system behavior & Fault detection and classification, adaptive protection schemes & Optimizing control parameters, real-time adjustments & Demand forecasting, load balancing \\ \cline{2-5}  & Modeling and predicting dynamic system responses, stability assessment & Pattern recognition for fault diagnosis, real-time fault location & Voltage and frequency control, predictive control strategies & Renewable energy integration, energy storage management \\ \hline \multirow{3}{*}{Fuzzy Logic} & Handling uncertainties in stability analysis decision-making under imprecise conditions & Adaptive protection settings, fault tolerance & Control of nonlinear systems, voltage control & Demand response management, grid management \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \end{tabular}
\end{table}