Abstract: A system for managing multiple power assets is provided. The system includes at least one volatile asset, at least one deterministic asset, and a controller communicatively coupled to the at least one volatile asset and the at least one deterministic asset, the controller configured to receive data from said at least one volatile asset, predict a change in power output for said at least one volatile asset based on the received data, and control operation of said at least one deterministic asset to compensate for the predicted change in power output.

Abstract: A system includes a model-based control system configured to receive data relating to parameters of a machinery via a plurality of sensors coupled to the machinery and select one or more models configured to generate a desired parameter of the machinery based on a determined relationship between the parameters and the desired parameter. The one or more models represent a performance of a device of the machinery. The model-based control system is configured to generate the desired parameter using the data and the one or more models control a plurality of actuators coupled to the machinery based on the desired parameter. Further, the model-based control system is configured to empirically tune the one or more models based on the data, the one or more parameters, and the desired parameter, compare the empirical tuning to a baseline tuning, and determine an operational state of the device based on the comparison.

Abstract: In accordance with one aspect of the present technique a method includes receiving at least one of a speed and an acceleration of a rotating component in a rotating machine. The method includes determining whether at least one of the speed and the acceleration of the rotating component exceeds a non-trip operating (NTO) space in a speed-acceleration plane, wherein the NTO space is based on a trip overshoot model. The method further includes sending a notification for tripping the rotating machine in response to determining that at least one of the speed and the acceleration of the rotating component exceeds the NTO space.

Abstract: A system includes a power generation system and a controller that controls the power generation system. The controller includes a processor that generates a model of the power generation system that estimates a value for a first parameter of the power generation system. The processor also receives a measured value of the first parameter. The processor further adjusts a correction factor of the model such that the estimated value of the first parameter output by the model is approximately equal to the measured value of the first parameter. The processor also generates a transfer function that represents the correction factor as a function of a second parameter of the power generation system. The processor further displays the transfer function along with one or more previously generated transfer functions to quantify degradation of the power generation system.

Abstract: A model-based control system is configured to select a desired parameter of a machinery configured to produce power and to output emissions and to select an emissions model configured to use the desired parameter as input and to output an emissions parameter. The model-based control system is additionally configured to tune the emissions model via a tuning system to derive a polynomial setpoint, and to control one or more actuators coupled to the machinery based on the polynomial setpoint.

Abstract: A system includes a power generation system and a controller that controls the power generation system. The controller includes a processor that generates a model of the power generation system that estimates a value for a first parameter of the power generation system. The processor also receives a measured value of the first parameter. The processor further adjusts a correction factor of the model such that the estimated value of the first parameter output by the model is approximately equal to the measured value of the first parameter. The processor also generates a transfer function that represents the correction factor as a function of a second parameter of the power generation system. The processor further displays the transfer function along with one or more previously generated transfer functions to quantify degradation of the power generation system.

Abstract: A system includes a model-based control system configured to receive data relating to parameters of a machinery via a plurality of sensors coupled to the machinery and select one or more models configured to generate a desired parameter of the machinery based on a determined relationship between the parameters and the desired parameter. The one or more models represent a performance of a device of the machinery. The model-based control system is configured to generate the desired parameter using the data and the one or more models control a plurality of actuators coupled to the machinery based on the desired parameter. Further, the model-based control system is configured to empirically tune the one or more models based on the data, the one or more parameters, and the desired parameter, compare the empirical tuning to a baseline tuning, and determine an operational state of the device based on the comparison.

Abstract: A system for managing multiple power assets is provided. The system includes at least one volatile asset, at least one deterministic asset, and a controller communicatively coupled to the at least one volatile asset and the at least one deterministic asset, the controller configured to receive data from said at least one volatile asset, predict a change in power output for said at least one volatile asset based on the received data, and control operation of said at least one deterministic asset to compensate for the predicted change in power output.

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 includes selecting a first desired parameter of a machinery configured to produce power, a first surrogate parameter related to the desired parameter, and a first model configured to generate the desired parameter based on a first relationship between the first surrogate parameter and the first desired parameter. The method also includes receiving data related to the first surrogate parameter from a plurality of sensors coupled to the machinery and generating the first desired parameter using the data and the first model. Further, the method includes deriving a first set of empirical data relating the first surrogate parameter to the desired parameter and adjusting the first model based on the data, the first surrogate parameter, and the first set of empirical data, wherein the adjustment to the first model occurs in real-time.

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: 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 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 includes a risk assessment system. The risk assessment system includes a risk calculation system configured to calculate a risk based on one or more static inputs and one or more dynamic inputs. The one or more dynamic inputs includes a location of a human resource, a mobile resource, or a combination thereof. The risk assessment system further includes a decision support system (DSS) configured to use the risk to derive one or more decisions based on the risk, the one or more static inputs, and the one or more dynamic inputs. The one or more decisions are configured to aid in operating an industrial facility.

Abstract: In accordance with one aspect of the present technique a method includes receiving at least one of a speed and an acceleration of a rotating component in a rotating machine. The method includes determining whether at least one of the speed and the acceleration of the rotating component exceeds a non-trip operating (NTO) space in a speed-acceleration plane, wherein the NTO space is based on a trip overshoot model. The method further includes sending a notification for tripping the rotating machine in response to determining that at least one of the speed and the acceleration of the rotating component exceeds the NTO space.

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: Embodiments of the present disclosure include systems and a method. In one embodiment, a system is provided. The system includes a risk calculation system configured to calculate a risk based on a static input and a dynamic input, and a decision support system configured to use the risk to derive a decision. The system also includes a plant control system configured to update operations of a plant based on the decision, wherein the decision predicts future plant conditions.

Abstract: Embodiments of the present invention have the technical effect of automatically testing an overspeed protection system of a powerplant machine comprising at least one shaft. An embodiment of the present invention may automatically test the overspeed protection system while the powerplant machine is decelerating from full-speed-no-load (FSNL). Another embodiment of the present invention may automatically test the overspeed protection system of the powerplant machine while the powerplant machine is accelerating to FSNL.

Abstract: A system includes a risk assessment system. The risk assessment system includes a risk calculation system configured to calculate a risk based on one or more static inputs and one or more dynamic inputs. The one or more dynamic inputs includes a location of a human resource, a mobile resource, or a combination thereof. The risk assessment system further includes a decision support system (DSS) configured to use the risk to derive one or more decisions based on the risk, the one or more static inputs, and the one or more dynamic inputs. The one or more decisions are configured to aid in operating an industrial facility.

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.