Abstract: In one embodiment, a method includes sensing first operations for one or more turbine systems in a fleet of turbine systems via a plurality of sensors disposed in the one or more turbine systems before a first wash operation. The method further includes sensing second operations for the one or more turbine systems via the one or more sensors after the water wash operation. The method also includes deriving at least one forecasting model based on the sensing first operations and the sensing second operations, wherein the at least one forecasting model is configured to predict a performance of a turbine system of the one or more turbine systems. The method additionally includes applying the at least one forecasting model to derive a predictive improvement in the performance for the turbine system.

Abstract: In one embodiment, a computing device includes one or more processors configured to execute instructions that cause the one or more processors to acquire pressure data measured by at least one pressure sensor disposed proximate to a filter house in an intake of a gas turbine engine system, derive an airflow or an air mass flow through a duct of the intake using a thermodynamic model of the gas turbine engine system based at least on the pressure data, derive an intake pressure drop in the duct using at least the pressure data, derive a loss parameter of the filter house by combining the air mass or air mass flow, and the intake pressure drop, derive a pressure loss model based on the loss parameter over a period of time, and determine a condition of the filter house based on the pressure loss model.

Abstract: A system includes an engine configured to generate power to drive a load. The system also includes a power augmentation system configured to augment a power output of the engine when the power augmentation system is activated. Additionally, the system includes a controller operatively coupled to the power augmentation system. The controller is configured to estimate a potential change in the power output of the engine caused by activation of the power augmentation system using a power augmentation model and an engine performance model.

Abstract: A system includes a processor configured to retrieve a sensor data, wherein the sensor data comprises one or more signals communicated from a plurality of sensors disposed in a power production system. The processor is further configured to group the sensor data into a first bin of data based on a degradation measure. The processor is additionally configured to quantify an uncertainty in the one or more bins of data, wherein the uncertainty comprises a difference between the one or more bins of data and a corrected data variation inference.

Abstract: A power production system includes a gas turbine system configured to combust a fuel to produce a power. The power production system further includes a lubrication system fluidly coupled to the gas turbine system and configured to move a lubricant through the gas turbine system during operations of the gas turbine system. The power production system also includes a processor communicatively coupled to the power production system. The processor is configured to receive input data from one or more sensors disposed on the gas turbine system, the lubrication system, or the combination thereof. The processor is further configured to execute one or more models to derive a condition of the lubrication system, the gas turbine system, or a combination thereof.

Abstract: In one embodiment, a method includes sensing first operations for one or more turbine systems in a fleet of turbine systems via a plurality of sensors disposed in the one or more turbine systems before a first wash operation. The method further includes sensing second operations for the one or more turbine systems via the one or more sensors after the water wash operation. The method also includes deriving at least one forecasting model based on the sensing first operations and the sensing second operations, wherein the at least one forecasting model is configured to predict a performance of a turbine system of the one or more turbine systems. The method additionally includes applying the at least one forecasting model to derive a predictive improvement in the performance for the turbine system.

Abstract: A system includes an engine configured to generate power to drive a load. The system also includes a power augmentation system configured to augment a power output of the engine when the power augmentation system is activated. Additionally, the system includes a controller operatively coupled to the power augmentation system. The controller is configured to estimate a potential change in the power output of the engine caused by activation of the power augmentation system using a power augmentation model and an engine performance model.

Abstract: In one embodiment, a computing device includes one or more processors configured to execute instructions that cause the one or more processors to acquire pressure data measured by at least one pressure sensor disposed proximate to a filter house in an intake of a gas turbine engine system, derive an airflow or an air mass flow through a duct of the intake using a thermodynamic model of the gas turbine engine system based at least on the pressure data, derive an intake pressure drop in the duct using at least the pressure data, derive a loss parameter of the filter house by combining the air mass or air mass flow, and the intake pressure drop, derive a pressure loss model based on the loss parameter over a period of time, and determine a condition of the filter house based on the pressure loss model.