## 2 The Need for AI in Power Systems over Traditional Techniques  
The integration of AI in power systems offers significant advantages over traditional techniques due to several key factors. First, AI is better suited to handle modern power systems' increasing complexity and scale. Traditional methods like state estimation and optimal power flow (OPF) have limitations when dealing with the vast amounts of data and intricate interdependencies in today's power grids. In contrast, AI techniques such as neural networks and machine learning can efficiently process large datasets and uncover complex patterns, as shown in the table summarizing AI applications [27]. Second, processing data and making real-time decisions is crucial for maintaining system stability and control. Traditional PID control and automatic generation control (AGC) techniques rely on predefined rules and parameters, which may not adapt quickly enough to real-time changes. AI methods, including deep learning and reinforcement learning, enable real-time data processing and adaptive decision-making, enhancing the system's responsiveness to dynamic conditions [28]. Predictive capabilities are another area where AI outperforms traditional approaches. Traditional methods like load shedding and frequency response analysis typically address issues reactively after they occur. As detailed in the AI techniques table, AI techniques like predictive analytics and time series analysis allow for accurate forecasting of future conditions, enabling proactive measures to maintain stability and prevent faults. Moreover, AI's ability to continuously learn and adapt is a significant advantage. Traditional techniques often involve fixed rules and settings, which might not be optimal under varying conditions. AI algorithms, particularly those involving reinforcement learning, can continuously improve by learning from new data and adjusting to changing system conditions. Handling uncertainty effectively is another reason for AI's superiority. Power systems face various uncertainties, such as fluctuating demand and renewable energy variability. Techniques like fuzzy logic and Bayesian networks, part of AI, can model and manage these uncertainties more robustly than traditional methods. Optimization capabilities provided by AI through metaheuristic algorithms like particle swarm optimization (PSO) and genetic algorithms (GAs) offer powerful solutions for complex problems in power systems. These algorithms can find optimal or near-optimal solutions for stability control, protection settings, and control actions, which are often challenging with traditional methods [29]. Finally, AI enhances monitoring and control through advanced tools like smart sensors and IoT devices. These tools provide real-time data that, when analyzed using AI, lead to more informed and effective control strategies, thereby improving the overall stability and protection of the power system. AI offers significant improvements over traditional techniques, which are presented in Table 1, in handling complexity, real-time processing, predictive accuracy, adaptability, optimization, and uncertainty management, making it indispensable for modern power system stability, protection, and control.