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Dry, low NOx gas turbines are extremely complex machines that are heavily relied upon in the power industry as baseload, cycling, and/or peaking units. These low-emission gas turbines present potential maintenance and monitoring challenges due to the intrinsically harsh pressure and temperature environments, which make diagnostics and prognostic capabilities extremely difficult. One such challenge involves understanding and interpreting combustion dynamics data. This paper focuses on gas turbine combustion dynamics monitoring (CDM) and describes an algorithm to determine combustor health based upon dynamic pressure. The ongoing CDM and diagnostic work has progressed from taking basic binned FFT data and transforming this data to statistically-based health indicators that can be continuously calculated to determine combustion system anomalies. These anomalies can be detected hours, days, and sometimes even weeks before passive CDM alarm levels are reached, thus, giving additional time to plan for shutdown, inspection, and repair. The paper will discuss real-time observed successes and challenges associated with combustor health monitoring, including sensor health determination and factors associated with the non-linear nature of combustion dynamics. Overall, this work is helping to better alleviate the user’s “black box” perspective of combustion dynamics monitoring systems through automated, real-time interpretation for combustion system health for can annular gas turbines. |
