General Electric Company

Mahesh Kumar Asati

Awadesh Kumar Tiwari

Achalesh Kumar Pandey

Scott Mordin Hoyte

Piero Patrone Bonissone

Raub Warfield Smith

Xiaoming Liu

Robert August Kaucic

Rasiklal Punjalal Shah

Vrinda Rajiv

Christopher Eugene Long

Manoharan Venugopal

Peter John Eisenzopf

Mahendhra Muthuramalingam

Megha Navalgund

Mark David Osborn

Joseph Manuel Portaz

Rajashekar Venkatachalam

Thomas P Schmitt

Prakash Mandayam Comare

Vikas Handa

A method for controlling and enhancing a startup operation for a combined cycle power block (block) having at least one gas turbine and at least one steam turbine, wherein operating parameters define performance and operational characteristics for the startup operation, the method comprising: receiving measured operating parameters from a plurality of reference blocks and, for each of the plurality of reference blocks, a plurality of types of the startup operations; given the measured operating parameters, developing one or more reference transfer functions between two of the measured operating parameters; receiving measured operating parameters from the startup operation of a target block; given the measured operating parameters from the target block, developing one or more transfer functions for the target block between two of the operating parameters; selecting one of the reference transfer functions; and normalizing the transfer function of the target block per the selected reference transfer function.

Methods and systems for enhancing operation of power plant generating units and systems

A method for optimizing a fleet of generating assets in which groupings include remotely located power blocks that operate so to collectively generate a fleet output level. The method may include: receiving real-time and historical measured values of operating parameters; for each of the generating assets, deriving a relational expression between the measured values of the process inputs and the measured values of the process outputs; defining a selected operating period; selecting competing operating modes for the fleet; based on the relational expressions and a generating configuration of the competing operating modes, calculating a result set for the operation of the fleet proposed during the selected operating period; defining a cost function; and evaluating each of the result sets pursuant to the cost function and, based thereupon, designating one of the competing operating modes as an optimized operating mode. The optimized operating mode may include a power sharing recommendation between the power blocks of the fleet.

Methods and systems for enhancing control of power plant generating units

A combined cycle power plant system that includes: a gas turbine operably connected to a heat recovery steam generator; a first controller for optimizing a shutdown operation defined between an initiating shutdown command and a terminating event, wherein the first is controller configured to execute the steps of: receiving the shutdown initiating command; receiving operating parameters measured by sensors for each of a first component and a second component of the combined cycle power plant, and, based thereupon, determining a current state for each of the first and the second component; based on the current state, deriving an optimized shutdown operating mode for transitioning the first component and the second component from the current state to a shutdown state. A second controller may be configured to: transform the optimized shutdown operating mode into commands configured to actuate mechanical control devices of the first component and the second component so that the shutdown operation proceeds according to the optimized shutdown operating mode.

A method for optimizing a generation of an output level over a selected operating period by a power block, wherein the power block comprises multiple gas turbines for collectively generating the output level. The control method may include: receiving current state data regarding measured operating parameters for each of the gas turbines of the power block; based on the current state data, defining competing operating modes for the power block; based on each of the competing operating modes, deriving a predicted value for a performance parameter regarding the operation of the power block over the selected operating period; determining a cost function and, pursuant thereto, evaluating the operation of the power block based on the predicted value of the performance parameter so to determine a projected cost; and comparing the projected costs from each of the optimized operating modes so to select therefrom an optimized operating mode.

A method for determining an operating set point for a combined cycle power plant, the method includes: simulating the operation of the power plant; correcting the simulation of the operation of the power plant; optimizing the simulation of the operation by simulating the operation at different operating settings and selecting at least one of the operating settings as being optimal, and generating the operating set point based on the optimized simulation of the power plant.

Method and system for optimization of combined cycle power plant

Systems and methods provided herein. In one embodiment, a system includes an advisory system including a loss computation engine configured to derive a total system loss for an industrial plant based on a first sensor positioned in a first industrial plant component and on a first physical model of the first industrial plant component. The advisory system further includes a cost model configured to use a cost function to derive a cost based on the total system loss, and a control strategy system configured to derive an advisory report, a control correction factor, or a combination thereof, based on the cost, wherein a control system is configured to apply the control correction factor to control a process in the industrial plant.

Plant control systems and methods

Systems and methods provided herein output an operational advisory based upon a plurality of models derived from a plurality of data, in which the plurality of models comprise at least one of a plant transfer function, a degradation contributor model, or a plant level cost model, and in which the plurality of data is derived from at least one of current plant data, historical plant data, business data, environmental data, component data, degradation contribution data, an optimization goal, or a combination thereof.

Plant control optimization system including visual risk display

Disclosed herein are methods and systems for advising and operating a power plant and related devices. In an embodiment, a power plant operator via a clientrequests from a serveradvisory information regarding a current power plant startup. The clientmay receive custom advisory information based on data of an initial state of the power plant and data from past power plant startups.

Method and system for managing plant operation

A method for automatically identifying defects in turbine engine blades is provided. The method comprises acquiring one or more radiographic images corresponding to one or more turbine engine blades and identifying one or more regions of interest from the one or more radiographic images. The method then comprises extracting one or more geometric features based on the one or more regions of interest and analyzing the one or more geometric features to identify one or more defects in the turbine engine blades.

Method for automatic identification of defects in turbine engine blades

A method for predicting a time to failure of a component in a system is presented. The method comprises obtaining a set of data measurements related to the component. The set of data measurements are representative of a plurality of parameters including a plurality of leading parameters. The method comprises generating a prediction model based upon the leading parameters considered in combination. The prediction model is then used to predict the time to failure of the component based on a set of real-time measurements, wherein the plurality of parameters are processed to predict the time to failure for the component. Finally, a confidence level for the predicted time to failure is determined based upon the plurality of parameters.

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Bengaluru, India

Mahesh Kumar Asati