## 5 Modulation of Intelligent Techniques for Power System Stability, Control and Protection
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **Description** & **Examples** \\ \hline \multirow{2}{*}{Demand Forecasting} & Predicts energy demand to optimize power generation and distribution. & Siemens and General Electric (GE) use AI algorithms to forecast energy demand accurately. \\ \hline \multirow{3}{*}{Grid Management} & Real-time monitoring and control of power grids to detect and predict faults, manage loads, and optimize electricity flow. & AI techniques are used in smart grid projects for real-time monitoring and fault detection. \\ \hline \multirow{2}{*}{Renewable Energy Integration} & Predicts the generation capacity of renewable sources based on weather conditions, improving grid stability and optimizing renewable use. & Google DeepMind collaborates with the UK National Grid to predict wind energy output. \\ \hline \multirow{2}{*}{Energy Storage Management} & Optimizes charging and discharging cycles of energy storage systems, prolonging battery life and reducing costs. & AI models manage battery energy storage systems for efficient use and extended battery life. \\ \hline \multirow{2}{*}{Predictive Maintenance} & Analyzes sensor data to predict equipment failures before they occur, reducing downtime and maintenance costs. & ABB and Schneider Electric use AI-driven predictive maintenance solutions to monitor equipment health in power plants and grids. \\ \hline \multirow{2}{*}{Energy Efficiency} & AI-driven systems in buildings and industries optimize energy use by learning consumption patterns and implementing efficiency measures. & Smart thermostats and building management systems use AI to reduce energy consumption. \\ \hline \multirow{2}{*}{Fault Detection and Diagnosis} & Quickly identifies and diagnoses faults in the power system, enabling faster responses and preventing equipment damage. & AI systems are employed in fault detection and diagnosis in smart grids. \\ \hline \multirow{2}{*}{Smart Grid Development} & Uses digital technology to monitor and manage electricity from all generation sources to meet varying electricity demands. & Smart grid projects globally incorporate AI for enhanced grid management and efficiency. \\ \hline \multirow{2}{*}{Customer Management and Services} & Enhances customer services through chatbots, predictive analytics for billing, and personalized energy-saving recommendations. & Utilities use AI for customer support chatbots and to provide personalized energy-saving tips. \\ \hline \multirow{2}{*}{Energy Trading} & Forecasts prices, optimizes trading strategies and manages risks in energy trading platforms. & For better financial outcomes and risk management, AI models are used in energy trading platforms. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Practical applications of AI in power systems [2; 27; 102]. is the superior method. The recently constructed buck-boost type Z-source inverter was controlled by five modified pulse width modulation schemes, which were compared and evaluated [105]. For uninterruptible power supply (UPS) systems, [PERSON] et al. [105] proposed a modified algorithm of pulse width modulation (PWM) inverter deadbeat control. It was confirmed that the proposed control scheme increased output voltage amplitude while offering a superior transient response and precise phase positioning for various load conditions. This technique is appropriate for high-power UPS applications where fine control of the power flow is needed, and the switching frequency is in the region of a few kilohertz. Two well-known methods for switching multilevel converters are phase-shifted pulse width modulation (PS-PWM) and level-shifted pulse width modulation (LS-PWM). Because it assures equal power distribution and switching frequency on all of the switches inside a particular module, PSPWM is typically selected for cascaded multilevel inverters. However, the absence of appropriate switching is a drawback of this method.