The rapid digitalization of modern electricity systems is transforming the operational architecture of transmission networks. Historically, transmission grid operations relied heavily on centralized control frameworks supported by Supervisory Control and Data Acquisition systems. These systems enabled operators to monitor network conditions, control equipment remotely, and maintain stable grid operation through human decision-making processes. While conventional SCADA infrastructures have served as the backbone of grid control for decades, the increasing complexity of modern power systems is exposing their operational limitations. The expansion of renewable energy generation, the electrification of transportation and industry, and the growing interconnection of regional electricity systems have introduced unprecedented variability into power system operations. Managing these increasingly dynamic conditions requires operational capabilities that extend beyond the monitoring and manual control functions traditionally provided by SCADA systems. As a result, transmission infrastructure is evolving toward intelligent grid architectures that integrate advanced sensing technologies, wide-area monitoring systems, and artificial intelligence–driven decision support platforms. This study examines the technological evolution of transmission grid control systems from conventional SCADA architectures to emerging AI-supported operational frameworks. The research explores the structural foundations of traditional grid control systems and analyzes the operational challenges that arise when managing highly complex power networks with conventional monitoring tools. The paper further investigates how digital transformation technologies, including phasor measurement units, intelligent electronic devices, and advanced communication networks, enhance real-time grid visibility and operational coordination. Artificial intelligence technologies are also examined as key enablers of next-generation transmission system management. Machine learning algorithms, predictive analytics platforms, and autonomous control systems are increasingly capable of analyzing large volumes of operational data and identifying patterns that support improved grid decision-making. These technologies enable functions such as predictive maintenance, congestion forecasting, anomaly detection, and automated operational optimization. The research highlights the potential for AI-assisted grid management to significantly enhance the reliability, efficiency, and resilience of transmission networks. However, the study also recognizes important challenges associated with cybersecurity risks, data integrity, and the integration of AI technologies into critical infrastructure systems. The findings demonstrate that the transition toward intelligent transmission system architectures will require a coordinated integration of digital monitoring technologies, advanced analytics platforms, and adaptive control mechanisms. By combining traditional operational expertise with AI-supported decision frameworks, future transmission networks will be better equipped to manage the increasing complexity of modern electricity systems.
Serhat Isikli (Wed,) studied this question.