International Journal of Scientific Research and Engineering Development

Power generation plants operate through highly complex systems that demand constant supervision, reliability, and safety assurance. Supervisory Control and Data Acquisition (SCADA) systems have traditionally served as central tools for monitoring and controlling industrial processes, but modern operational risks require them to go beyond basic automation. This paper presents the design and implementation of a Safety-Integrated SCADA (SI-SCADA) framework specifically tailored for process hazard control in power generation environments. The proposed approach integrates safety logic, fault-tolerant communication, and predictive analytics into the SCADA architecture to ensure early detection, isolation, and mitigation of hazardous events. The study follows the guidelines of IEC 61511 and ISO 13849 safety standards, incorporating redundancy and real-time data analysis to enhance both system availability and operator awareness. Machine learning models and hazard operability (HAZOP) analysis are applied to predict critical failures such as boiler overpressure, turbine overspeed, and cooling system malfunctions. The framework was validated through simulations using a digital-twin environment that replicates combined-cycle and thermal power plants. Results show that integrating safety mechanisms directly into SCADA operations can reduce hazard response time by approximately 40%, achieve a Safety Integrity Level (SIL 3) rating, and increase plant uptime beyond 99.7%. The findings demonstrate that SI-SCADA offers a scalable, standards-compliant pathway toward safer, smarter, and more resilient power generation systems.