## 4 Applications of AI Techniques in Power Systems
### Protection  
Integrating artificial intelligence (AI) into power system protection has revolutionized how modern power systems operate, offering substantial improvements in reliability, speed, and precision. AI techniques such as machine learning, neural networks, fuzzy logic, and expert systems are extensively utilized to enhance power system protection. For instance, machine learning models, including support vector machines (SVMs) and decision trees, are employed for fault detection and classification, providing higher accuracy and faster identification of faults by analyzing vast amounts of historical and real-time data. Neural networks, including intense learning models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are instrumental in real-time monitoring and control, enabling the system to learn from data patterns and make informed decisions quickly [100].  
Fuzzy logic systems are used in adaptive relaying, where they adjust relay settings dynamically to account for uncertainties in power system parameters, ensuring optimal performance under varying conditions. Expert systems, which incorporate knowledge from human experts, are pivotal in accurately locating faults within transmission and distribution networks, significantly reducing the time needed for repairs. Genetic algorithms and other optimization techniques facilitate the coordination of protection devices by finding the best settings to minimize outage areas and enhance system reliability. Predictive analytics, powered by AI, plays a crucial role in predictive maintenance by analyzing sensor data to forecast potential equipment failures, allowing for proactive interventions and reducing downtime. Furthermore, AI-driven intrusion detection systems (IDSs) are essential for cybersecurity, detecting and responding to cyber threats that target power system protection infrastructure [101]. Table 6 showcases AI's practical applications in power systems.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Type of Stability** & **Focus Area** & **Control Types** & **Associated Problems** \\ \hline Angular Stability & \begin{tabular}{c} Rotor angular stability at small signals \\ \end{tabular} & \begin{tabular}{c} Damping controllers, power system \\ stabilizers (PSSs) \\ \end{tabular} & \begin{tabular}{c} Oscillations in rotor angle, \\ loss of synchronism \\ \end{tabular} \\ \hline Voltage Stability & \begin{tabular}{c} Maintaining acceptable \\ voltage levels \\ \end{tabular} & \begin{tabular}{c} Voltage regulators, static VAR \\ compensators (SVCs), FACTS devices \\ (flexible AC transmission systems) \\ \end{tabular} & \begin{tabular}{c} Voltage collapse, voltage \\ fluctuations \\ \end{tabular} \\ \hline Frequency Stability & Maintaining system frequency & \
\begin{tabular}{c} Frequency controllers, load frequency \\ control (LFC), automatic generation \\ control (AGC) \\ \end{tabular} &
\begin{tabular}{c} Frequency deviations, \\ frequency oscillations \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Types of control and focus areas for stability in power systems [32; 95].