(153b) Autonomous Tuning and Firing Temperature Control for Variable Engine Operating Conditions - a New Control Scheme Designed for Optimized Engine Control in All Seasons and a Variety of Fuel Gas Compositions

Abstract

Siemens has developed a gas turbine control optimization platform called Gas Turbine Autonomous Control Optimizer (GT-ACO), designed to optimize various gas turbine control functions and deliver enhanced value to our customers.

With the advent of premixed dry low NOx and ultra-low NOx combustion systems NOx, CO and particulate emissions from gas fired power generation turbines have been reduced. However, premixed combustion systems have presented challenges, one of which is addressing combustion dynamics. Combustion dynamics are driven by rapid pressure fluctuations within the combustor. A pilot or diffusion flame is typically used to control combustion dynamics. However, to reduce emissions, the diffusion flame must be minimized. The first module of the GT-ACO 2.0 platform, Combustion Optimization, includes use of data-driven reinforcement learning technology to perform closed loop dynamics/NOx control optimization in order to moderate emissions while keeping combustion dynamics within targeted limits with no other changes to combustion hardware.

In addition, gas turbine controls have used exhaust temperature measurements correlated to turbine inlet temperatures to control the unit. Such methodologies typically rely on assumptions for cooling air flows, inlet and exhaust conditions, and can be influenced by transient conditions. Improvements can be made using a physics-based real time thermal model (RTTM) to calculate and control directly to turbine inlet temperature (TIT), which can reduce under- and over-firing conditions during engine operation and provide flexibility for operation with varying fuel gas content.

GT-ACO 2.0 has been developed as a platform to improve control of the gas turbine for the cases described above. However, there are many more modules that can be developed since it uses the Siemens Synalytics platform to transform big data into useable data in support of optimization. By applying such technologies, Siemens is can deliver more intelligent, proactive and customized services that can translate into real customer value through improved service offerings, customer experience, and introduction of the next generation of products.

This paper will dive deeper into the GT-ACO platform and describe how application of this new technology can help optimize gas turbine operation.