Abstract: Systems and methods are provided for the control of an industrial asset, such as a power generating asset. Accordingly, a cyber-attack model predicts a plurality of operational impacts on the industrial asset resulting from a plurality of potential cyber-attacks. The cyber-attack model also predicts a corresponding plurality of potential mitigation responses. In operation, a cyber-attack impacting at least one component of the industrial asset is detected via the cyber-attack neutralization module and a protected operational impact of the cyber-attack is identified based on the cyber-attack model. The cyber-attack neutralization module selects at least one mitigation response of the plurality of mitigation responses based on the predicted operational impact and an operating state of the industrial asset is altered based on the selected mitigation response.

Abstract: A method for providing backup control for a supervisory controller of at least one wind turbine includes observing, via a learning-based backup controller of the at least one wind turbine, at least one operating parameter of the supervisory controller under normal operation. The method also includes learning, via the learning-based backup controller, one or more control actions of the at least one wind turbine based on the operating parameter(s). Further, the method includes receiving, via the learning-based backup controller, an indication that the supervisory controller is unavailable to continue the normal operation. Upon receipt of the indication, the method includes controlling, via the learning-based backup controller, the wind turbine(s) using the learned one or more control actions until the supervisory controller becomes available again. Moreover, the control action(s) defines a delta that one or more setpoints of the wind turbine(s) should be adjusted by to achieve a desired outcome.

Abstract: Systems and methods are provided for the control of an industrial asset, such as a power generating asset. Accordingly, an interceptor module receives a state-change instruction from a state module that directs a change from a first state condition to a second state condition. The first and second state conditions direct modes of operation of at least one sub module of the controller of the industrial asset. The interceptor module then correlates the state-change instruction to a state-change classification. Based on the state-change classification, the interceptor module identifies an indication of a mode-switching attack. In response to the identification of the mode-switching attack, at least one mitigation response is implemented.

Abstract: A system for computing wind turbine estimated operational parameters and/or control commands, includes sensors monitoring the wind turbine, a control processor implementing a model performing a linearization evaluation to obtain a structural component dynamic behavior, a fluid component dynamic behavior, and/or a combined structural and fluid component dynamic behavior of wind turbine operation, and a module performing a calculation utilizing the linearization evaluation of the structural component dynamic behavior, the fluid component dynamic behavior, and/or the combined structural and fluid component dynamic behavior. The module being at least one of an estimation module and a multivariable control module. The estimation module generating signal estimates of turbine or fluid states. The multivariable control module determining actuator commands that include wind turbine commands that maintain operation of the wind turbine at a predetermined setting in real time.

Abstract: A method for detecting a cyberattack on a control system of a wind turbine includes providing a plurality of classification models of the control system. The method also includes receiving, via each of the plurality of classification models, a time series of operating data from one or more monitoring nodes of the wind turbine. The method further includes extracting, via the plurality of classification models, a plurality of features using the time series of operating data. Each of the plurality of features is a mathematical characterization of the time series of operating data. Moreover, the method includes generating an output from each of the plurality of classification models and determining, using a decision fusion module, a probability of the cyberattack occurring on the control system based on a combination of the outputs. Thus, the method includes implementing a control action when the probability exceeds a probability threshold.

Abstract: Systems and methods are provided for the control of an industrial asset, such as a power generating asset. Accordingly, a cyber-attack model predicts a plurality of operational impacts on the industrial asset resulting from a plurality of potential cyber-attacks. The cyber-attack model also predicts a corresponding plurality of potential mitigation responses. In operation, a cyber-attack impacting at least one component of the industrial asset is detected via the cyber-attack neutralization module and a protected operational impact of the cyber-attack is identified based on the cyber-attack model. The cyber-attack neutralization module selects at least one mitigation response of the plurality of mitigation responses based on the predicted operational impact and an operating state of the industrial asset is altered based on the selected mitigation response.

Abstract: A method for controlling an energy generation and storage system using a multi-layer architecture is provided. The method includes determining, by one or more control devices, a power or energy generation for the energy generation and storage system at a first layer of the multi-layer architecture. The method includes determining, by the one or more control devices, a power or energy set point for the system at a second layer of the multi-layer architecture. The method includes controlling, by the one or more control devices, the energy generation and storage system based, at least in part, on the power or energy setpoint.

Abstract: Systems and methods are provided for the robust, multivariable control of a power generating asset via H-infinity loop shaping using coprime factorization. Accordingly, a controller of the power generating asset computes a gain value for an H-infinity (H?) module in real-time at predetermined sampling intervals using an actuator dynamic model. The controller then determines an acceleration factor based, at least in part, on the gain value of the H? module. Based, at least in part on the acceleration vector, the controller generates a control vector. An operating state of at least one component of the power generating asset is changed based on the control vector.

Abstract: Systems and methods are provided for the robust, multivariable control of a power generating asset via H-infinity loop shaping using coprime factorization. Accordingly, a controller of the power generating asset computes a gain value for an H-infinity (H?) module in real-time at predetermined sampling intervals using an actuator dynamic model. The controller then determines an acceleration factor based, at least in part, on the gain value of the H? module. Based, at least in part on the acceleration vector, the controller generates a control vector. An operating state of at least one component of the power generating asset is changed based on the control vector.

Abstract: A wind turbine is provided. The wind turbine includes a mechanical system, an electrical system and a controller. The controller is for determining an electrical capability limit of the electrical system according at least in part to one or more operating conditions of the wind turbine and one or more environment conditions of a site of the wind turbine, comparing the electrical capability limit of the electrical system and a mechanical capability limit of the mechanical system, and controlling the electrical system to operate at the smaller one of the electrical capability limit and the mechanical capability limit. A method for controlling a wind turbine comprising a mechanical system and an electrical system is also provided.

Abstract: A system for wind turbine control includes a state dependent quadratic regulator (SDQR) control unit, a linear quadratic regulator (LQR) generating control acceleration commands for wind turbine speed and wind turbine power regulation, an actuator dynamic model computing a gain value for the LQR at predetermined sampling intervals and augmenting the actuator dynamic model with a wind turbine model. The wind turbine model either an analytical linearization model or a precomputed linear model, where the precomputed linear model is selected from a model bank based on a real-time scheduling operation, and the analytical linearization model is computed using an online linearization operation in real-time at time intervals during operation of the wind turbine based on current wind turbine operating point values present at about the time of linearization. A method and a non-transitory medium are also disclosed.

Abstract: A wind turbine is provided. The wind turbine includes a mechanical system, an electrical system and a controller. The controller is for determining an electrical capability limit of the electrical system according at least in part to one or more operating conditions of the wind turbine and one or more environment conditions of a site of the wind turbine, comparing the electrical capability limit of the electrical system and a mechanical capability limit of the mechanical system, and controlling the electrical system to operate at the smaller one of the electrical capability limit and the mechanical capability limit. A method for controlling a wind turbine comprising a mechanical system and an electrical system is also provided.

Abstract: A system for computing wind turbine estimated operational parameters and/or control commands, includes sensors monitoring the wind turbine, a control processor implementing a model performing a linearization evaluation to obtain a structural component dynamic behavior, a fluid component dynamic behavior, and/or a combined structural and fluid component dynamic behavior of wind turbine operation, and a module performing a calculation utilizing the linearization evaluation of the structural component dynamic behavior, the fluid component dynamic behavior, and/or the combined structural and fluid component dynamic behavior. The module being at least one of an estimation module and a multivariable control module. The estimation module generating signal estimates of turbine or fluid states. The multivariable control module determining actuator commands that include wind turbine commands that maintain operation of the wind turbine at a predetermined setting in real time.

Abstract: A system for wind turbine control includes a state dependent quadratic regulator (SDQR) control unit, a linear quadratic regulator (LQR) generating control acceleration commands for wind turbine speed and wind turbine power regulation, an actuator dynamic model computing a gain value for the LQR at predetermined sampling intervals and augmenting the actuator dynamic model with a wind turbine model. The wind turbine model either an analytical linearization model or a precomputed linear model, where the precomputed linear model is selected from a model bank based on a real-time scheduling operation, and the analytical linearization model is computed using an online linearization operation in real-time at time intervals during operation of the wind turbine based on current wind turbine operating point values present at about the time of linearization. A method and a non-transitory medium are also disclosed.

Abstract: A method for building a model-based control solution is disclosed. The method includes obtaining, via a model-based control definition sub-unit, a first set of component models from a component model library and defining, via the model-based control definition sub-unit, a system model by interconnecting the first set of component models. Also, the method includes obtaining, via the model-based control definition sub-unit, a first model-based analytic algorithm from a model-based analytic algorithm library and associating, via the model-based control definition sub-unit, the first model-based analytic algorithm with the system model to generate the model-based control solution.

Abstract: A method for controlling an energy generation and storage system using a multi-layer architecture is provided. The method includes determining, by one or more control devices, a power or energy generation for the energy generation and storage system at a first layer of the multi-layer architecture. The method includes determining, by the one or more control devices, a power or energy set point for the system at a second layer of the multi-layer architecture. The method includes controlling, by the one or more control devices, the energy generation and storage system based, at least in part, on the power or energy setpoint.

Abstract: A method for building a model-based control solution is disclosed. The method includes obtaining, via a model-based control definition sub-unit, a first set of component models from a component model library and defining, via the model-based control definition sub-unit, a system model by interconnecting the first set of component models. Also, the method includes obtaining, via the model-based control definition sub-unit, a first model-based analytic algorithm from a model-based analytic algorithm library and associating, via the model-based control definition sub-unit, the first model-based analytic algorithm with the system model to generate the model-based control solution.

Abstract: In accordance with one aspect of the present technique, a method is disclosed. The method includes modifying one or more operational parameters of a gas turbine (GT) to increase an exhaust gas temperature above a standard start-up temperature. The method also includes receiving at least one of GT operational data, heat recovery steam generator (HRSG) operational data, and steam turbine (ST) operational data from a plurality of sensors. The method further includes predicting a ST roll-off time based on at least one of the GT operational data, the HRSG operational data, and the ST operational data. The method further includes modifying the one or more operational parameters of the GT to satisfy one or more ST roll-off permissives at the predicted ST roll-off time.

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 method implemented using at least one processor includes receiving a plurality of measured operational parameters of a turbo machine having a rotor and a stator. The plurality of measured operational parameters includes a plurality of real-time operational parameters and a plurality of stored operational parameters. The method further includes generating a finite element model of the turbo machine and generating a plurality of snapshots based on the finite element model and the plurality of stored operational parameters. The method further includes generating a reduced order model based on the plurality of snapshots. The method also includes determining an estimated clearance between the rotor and the stator during operation of the turbo machine, based on the reduced order model and the plurality of real-time operational parameters.