General Electric Company

Ge Infrastructure Technology, LLC

General Electric Renovables Espana, S.l.

Fernando Javier D'Amato

Masoud Abbaszadeh

Daniel Francis Holzhauer

Christopher Eugene Long

Fabiano Daher Adegas

Vivek Venugopal Badami

Mustafa Tekin Dokucu

Ramu Sharat Chandra

Kalpesh Singal

Hema Kumari Achanta

Jitendra Kumar

Justin Barton

Georgios Boutselis

Weizhong Yan

Rajni Kant Burra

Enno Ubben

Zhuohui Tan

Patrick Hammel Hart

Xu Fu

Irene Michelle Berry

Aditya Kumar

Catherine Mary Graichen

Deepak Raj Sagi

Conner Brooks Shane

Maria Cecilia Mazzaro

Hullas Sehgal

Charles Joseph Kosuth

Alina Fatima Moosvi

Dean Alexander Baker

Bo Qu

Xiongzhe Huang

Darrin Glen Kirchhof

Vaidhya Nath Venkitanarayanan

Mahalakshmi Shunmugham Balasubramaniam

Matthew John Mosley

Shuang Gu

Roopesh Bhaskaran Nagathil

Rohan Saraswat

Yuh-Shyang Wang

Emrah Biyik

Susan Ruth Peterson

Junqiang Zhou

Souransu Nandi

Megan Ann DeWitt

Rodrigo Lopez-Negrete De La Fuente

Ratna Manedhar Punjala

Luis Blasini

Zhaoyuan Yang

Alexander Luenenschloss

Karla Kvaternik

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.

Learning-based backup controller for a wind turbine

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.

System and method for cyberattack detection in a wind turbine control system

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.

Systems and methods for controlling an industrial asset in the presence of a cyber-attack

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. A method and a non-transitory medium are also disclosed.

Wind turbine model based control and estimation with accurate online models

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.

System and method for controlling operation of an energy generation and storage system

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&#x221e;) 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&#x221e; 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.

Systems and methods for multivariable control of a power generating system

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.

Wind turbine and method for controlling wind turbine

A cyber-physical system may have a plurality of monitoring nodes each generating a series of current monitoring node values over time that represent current operation of the cyber-physical system. According to some embodiments, a features extraction computer platform may receive the series of current monitoring node values over time and generate current feature vectors based on the series of current monitoring mode values. A system mode estimation computer platform may provide the current feature vectors to a probabilistic graphical model to generate an estimated system mode. The system mode estimation computer platform may then compare the estimated system mode with a currently reported system mode output by the cyber-physical system and generate a system mode status indication based on a result of said comparison. According to some embodiments, the system mode status indication can be used to override the currently reported system mode of the cyber-physical system.

Detection and protection against mode switching attacks in cyber-physical systems

A cyber-physical system may have a plurality of monitoring nodes each generating a series of current monitoring node values over time representing current operation of the system. A data-driven features extraction computer platform may receive the series of current monitoring node values and generate current data-driven feature vectors based on the series of current monitoring node values. A residual features extraction computer platform may receive the series of current monitoring node values, execute a system model and utilize a stochastic filter to determine current residual values, and generate current residual-driven feature vectors based on the current residual values. An abnormal detection platform may then receive the current data-driven and residual-driven feature vectors and compare the current data-driven and residual-driven feature vectors with at least one decision boundary associated with an abnormal detection model. An abnormal alert signal may then be transmitted when appropriate based on a result of said comparison.

Hybrid feature-driven learning system for abnormality detection and localization

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.

System and method for wind turbine multivariable control using state dependent quadratic regulator

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.

Systems and methods for building a model-based control solution

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.

System and method for start-up of a combined cycle power plant

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.

Virtual sensor systems and methods for estimation of steam turbine sectional efficiencies

Certain embodiments of the invention may include systems and methods for predicting transient operational characteristics of a power plant. According to an example embodiment of the invention, a method is provided for predicting transient operational characteristics of a power plant. The method can include receiving operating information associated with the power plant; using a power plant dynamics model for determining a current operating state of the power plant based at least in part on the received operating information; and using the power plant dynamics model and a power plant controls model for predicting one or more future operating parameters associated with power plant based at least in part on the determined current operating state and the received operating information.

Systems and methods for predicting transient operational characteristics of a power plant

A control system for a combined cycle power generation system including a gas turbine engine (GT), a heat recovery steam generator (HRSG), and a steam turbine (ST) includes a display wherein an operator may observe information about predicted operating parameters; a user interface wherein an operator may provide additional operating constraints; and a controller configured to generate input profiles of the GT, the HRSG, and the ST that satisfy the nominal constraints and any additional constraints and to generate the information about the predicted operating parameters. The controller may be configured to detect a stage transition of power generation system operation and update the input profiles. The controller may be configured to generate alternative operating scenarios by mapping alternative control actions to an operating constraint of at least one of the system components.

System for controlling input profiles of combined cycle power generation system

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Niskayuna, NY, United States of America

Fernando Javier D'Amato