Abstract: A system for improved reliability operations of a system with a heat recovery steam generator (HRSG) are presented. The system includes a processor configured to execute a model library to model a safety system. The model library includes a plurality of subsystem models each configured to derive a reliability measure. The system also includes the HRSG and an HRSG advisory system. The HRSG advisory system may be executed by the processor and is configured to receive one or more condition monitoring algorithm results, receive one or more measurements; and determine a probability of failure for the HRSG based at least in part on the one or more condition monitoring algorithm results, the one or more measurements, the model library, or a combination thereof. Moreover, determining the probability of failure includes determining a most likely state of a plurality of states of the HRSG.

Abstract: Systems and methods for improved control of a turbomachine system with a bottoming cycle system are presented. The systems and methods include a controller that utilizes modeling techniques to derive a plurality of load path curves. The controller utilizes a current load path, a minimum load path, and a constant efficiency load path. The systems and methods include a control process configured to receive a user input representative of a life cycle control modality and to execute a control action based on deriving a load efficiency by applying the current load path, the minimum load path, the constant efficiency load path, or a combination thereof, and the life cycle control modality. The control action is applied to control the turbomachine system and the bottoming cycle system fluidly coupled to the turbomachine system. Further, the life cycle control modalities may be selected by a user based upon known tradeoffs.

Abstract: A method of controlling a heat recovery steam generator (HRSG) includes measuring a first regulated output of the HRSG and a second regulated output of the HRSG. The method includes comparing the first regulated output to a first setpoint defining a first target output to generate a first error signal and comparing the second regulated output to a second setpoint defining a second target output to generate a second error signal. The method also includes generating, by a controller implementing a multivariable control algorithm having as inputs the first error signal and the second error signal, control signals to control the HRSG to adjust values of the first regulated output and the second regulated output.

Abstract: Systems, tangible non-transitory machine readable computer media, and methods are provided. In one embodiment a system includes an industrial controller having at least one processor configured to: receive a measured input from a turbomachinery having a compressor, execute a hybrid model of the compressor, receive a measured output, compare the measured input to the measured output to derive an error value, perform a signature analysis if the error value is beyond a range; and derive a probability of compressor stall based on the signature analysis.

Abstract: A system may include a model library configured to model a system, wherein the model library includes a plurality of subsystem models, and each of the plurality of subsystem models is configured to derive a reliability measure. The system further includes a fault tolerance input and a maintenance policy input. The system further includes a dynamic risk calculation engine (DRCE) configured to use a user-defined set of the plurality of subsystem models, the fault tolerance input and the maintenance policy input, to derive a system risk for an apparatus.

Abstract: Systems and methods of estimating an efficiency of a section of a steam turbine are disclosed. The systems and methods include determining a set of measurement data obtained directly from a set of sensors on the steam turbine, determining a set of derived data relating to measurements that cannot be obtained directly from the set of sensors, and estimating the efficiency of the section using the set of measurement data and the set of derived data. The systems and methods disclosed use physics-based models combined with nonlinear filtering techniques to estimate steam turbines' efficiencies when physical sensors are not available. These models capture the behavior of different components of the power plant, including all steam turbine sections, admission and crossover pipes, flow junctions, admission and control valves.

Abstract: A system may include a model library configured to model a safety system, wherein the model library comprises a plurality of subsystem models, and each of the plurality of subsystem models is configured to derive a reliability measure. The system further includes a fault tolerance input and a maintenance policy input. The system further includes a dynamic risk calculation engine (DRCE) configured to use a user-defined set of the plurality of subsystem models, the fault tolerance input and the maintenance policy input, to derive a system risk for an apparatus.

Abstract: A control system for monitoring a pressure vessel includes a pressure sensor coupled to a first pressure vessel component. The system also includes a level sensor coupled to a second pressure vessel component. The system additionally includes at least one computing device including at least one input channel configured to receive data from the pressure sensor and the level sensor and a processor coupled to the at least one input channel. The processor is programmed to populate a level and pressure dynamics model associated with the pressure vessel with data received from the pressure sensor and the level sensor, the processor further programmed to be capable of controlling operation of the pressure vessel based on the level and pressure dynamics model.

Abstract: Systems and methods for improved control of a turbomachine system with a bottoming cycle system are presented. The systems and methods include a controller that utilizes modeling techniques to derive a plurality of load path curves. The controller utilizes a current load path, a minimum load path, and a constant efficiency load path. The systems and methods include a control process configured to receive a user input representative of a life cycle control modality and to execute a control action based on deriving a load efficiency by applying the current load path, the minimum load path, the constant efficiency load path, or a combination thereof, and the life cycle control modality. The control action is applied to control the turbomachine system and the bottoming cycle system fluidly coupled to the turbomachine system. Further, the life cycle control modalities may be selected by a user based upon known tradeoffs.

Abstract: A system may include a dynamic risk calculation engine (DRCE) system. The DRCE includes a model library configured to model a system, wherein the model library comprises a plurality of subsystem models, and each of the plurality of subsystem models is configured to derive a reliability measure. The DRCE further includes a fault tolerance input and a maintenance policy input. The DRCE additionally includes a run-time risk calculation engine configured to use a user-defined set of the plurality of subsystem models, the fault tolerance input, and the maintenance policy input, to derive a system risk for an apparatus.

Abstract: A system for improved reliability operations of a system with a heat recovery steam generator (HRSG) are presented. The system includes a processor configured to execute a model library to model a safety system. The model library includes a plurality of subsystem models each configured to derive a reliability measure. The system also includes the HRSG and an HRSG advisory system. The HRSG advisory system may be executed by the processor and is configured to receive one or more condition monitoring algorithm results, receive one or more measurements; and determine a probability of failure for the HRSG based at least in part on the one or more condition monitoring algorithm results, the one or more measurements, the model library, or a combination thereof. Moreover, determining the probability of failure includes determining a most likely state of a plurality of states of the HRSG.

Abstract: A method of controlling a heat recovery steam generator (HRSG) includes measuring a first regulated output of the HRSG and a second regulated output of the HRSG. The method includes comparing the first regulated output to a first setpoint defining a first target output to generate a first error signal and comparing the second regulated output to a second setpoint defining a second target output to generate a second error signal. The method also includes generating, by a controller implementing a multivariable control algorithm having as inputs the first error signal and the second error signal, control signals to control the HRSG to adjust values of the first regulated output and the second regulated output.

Abstract: A control system for monitoring a pressure vessel includes a pressure sensor coupled to a first pressure vessel component. The system also includes a level sensor coupled to a second pressure vessel component. The system additionally includes at least one computing device including at least one input channel configured to receive data from the pressure sensor and the level sensor and a processor coupled to the at least one input channel. The processor is programmed to populate a level and pressure dynamics model associated with the pressure vessel with data received from the pressure sensor and the level sensor, the processor further programmed to be capable of controlling operation of the pressure vessel based on the level and pressure dynamics model.

Abstract: A turbine performance diagnostic system creates a performance report for one or more turbines and includes an assessment module that receives operating data from at least one turbine and produces a performance report from the operating data. The assessment module includes a change detection module configured to determine when at least one parameter has changed beyond an associated threshold and generate an alarm and a root cause analyzer coupled to the change detection module that predicts a root cause of the alarm utilizing a Bayesian Belief Network (BBN). The performance report includes an indication of the predicted root cause.

Abstract: A system may include a model library configured to model a system, wherein the model library comprises a plurality of subsystem models, and each of the plurality of subsystem models is configured to derive a reliability measure. The system further includes a fault tolerance input and a maintenance policy input. The system further includes a dynamic risk calculation engine (DRCE) configured to use a user-defined set of the plurality of subsystem models, the fault tolerance input and the maintenance policy input, to derive a system risk for an apparatus.

Abstract: A system may include a dynamic risk calculation engine (DRCE) system. The DRCE includes a model library configured to model a system, wherein the model library comprises a plurality of subsystem models, and each of the plurality of subsystem models is configured to derive a reliability measure. The DRCE further includes a fault tolerance input and a maintenance policy input. The DRCE additionally includes a run-time risk calculation engine configured to use a user-defined set of the plurality of subsystem models, the fault tolerance input, and the maintenance policy input, to derive a system risk for an apparatus.

Abstract: A system may include a model library configured to model a safety system, wherein the model library comprises a plurality of subsystem models, and each of the plurality of subsystem models is configured to derive a reliability measure. The system further includes a fault tolerance input and a maintenance policy input. The system further includes a dynamic risk calculation engine (DRCE) configured to use a user-defined set of the plurality of subsystem models, the fault tolerance input and the maintenance policy input, to derive a system risk for an apparatus.

Abstract: A system for selecting a representative sensor set of a power plant is provided including a computer. The computer has a memory. The memory stores a list of all feasible sensor sets, and at least one variable that is used to calculate a local minimum value of a cost function and a cost function value. The computer includes control logic for executing an algorithm. The computer includes control logic for selecting a proposed sensor set from the list of all feasible sensor sets. The proposed sensor set is based on a specific iteration of the algorithm. The control module includes control logic for determining if the system is observable using the proposed sensor set. The control module includes control logic for determining if the cost function value is less than the local minimum value of the cost function, if the system is observable using the proposed sensor set.

Abstract: A system, in one embodiment, provides means for acquired measurements of one or more operating parameters of a turbine engine. The system further includes means for determining an estimated value of a performance parameter of interest at a current time based at least partially upon the acquired measurements, means for adjusting a deterioration model at the current time based on a historical set of estimated values from previous times, and means for forecasting performance changes in the turbine engine based on the adjusted deterioration model.

Abstract: Systems and methods of estimating an efficiency of a section of a steam turbine are disclosed. The systems and methods include determining a set of measurement data obtained directly from a set of sensors on the steam turbine, determining a set of derived data relating to measurements that cannot be obtained directly from the set of sensors, and estimating the efficiency of the section using the set of measurement data and the set of derived data. The systems and methods disclosed use physics-based models combined with nonlinear filtering techniques to estimate steam turbines' efficiencies when physical sensors are not available. These models capture the behavior of different components of the power plant, including all steam turbine sections, admission and crossover pipes, flow junctions, admission and control valves.