Problems of integrating artificial intelligence with SCADA systems

The object of research: This study examines the integration of artificial intelligence (AI) with SCADA (Supervisory Control and Data Acquisition) systems in power plants to enhance automation and operational efficiency. Investigated problem: Key challenges include real-time data processing limitations, system incompatibilities, and security risks. The lack of standardized AI implementation frameworks further complicates seamless integration. The main scientific results: AI-driven SCADA systems improve predictive maintenance, optimize energy management, and enhance decision-making. Machine learning models enable early fault detection and anomaly identification, reducing downtime and maintenance costs while improving system performance. The area of practical use of the research results: The findings are applicable to industries using SCADA, including power generation, water distribution, and oil and gas. AI-SCADA integration enhances automation, efficiency, and predictive maintenance. Innovative technological product: An AI-enhanced SCADA framework utilizing machine learning and advanced analytics to improve monitoring, anomaly detection, and system optimization. Scope of the innovative technological product: This AI-integrated SCADA solution is applicable across industrial sectors, enabling smarter automation, enhanced security, and data-driven decision-making, particularly in energy and industrial automation