Optimizing Gas and Steam Turbine Performance Through Predictive Maintenance and Thermal Optimization for Sustainable and Cost-Effective Power Generation

Abstract

The performance of gas and steam turbines plays a pivotal role in the efficiency and sustainability of power generation systems. This review explores innovative approaches to optimizing turbine performance through predictive maintenance and thermal optimization, with a focus on enhancing the sustainability and cost-effectiveness of power plants. Predictive maintenance, leveraging advanced data analytics, machine learning algorithms, and Internet of Things (IoT) technologies, enables early detection of turbine faults and performance degradation, thereby reducing downtime and maintenance costs. Thermal optimization techniques, such as advanced cooling systems, improved heat recovery processes, and optimized combustion strategies, are essential for maximizing the thermal efficiency of turbines and minimizing energy losses. The integration of both strategies—predictive maintenance and thermal optimization—enables power plants to achieve optimal performance, reduce fuel consumption, extend the lifespan of turbines, and contribute to the reduction of carbon emissions. This paper also examines case studies and the application of these technologies in the context of modern gas and steam turbine systems, providing insights into their potential to drive sustainable and cost-effective power generation solutions. Furthermore, challenges such as high capital investment, technological complexity, and the need for skilled workforce development are discussed, along with recommendations for overcoming these barriers to achieve the full potential of predictive maintenance and thermal optimization.