General Electric Company

John Joseph Raffensperger

William Forrester Seely

Karl Dean Minto

Benjamin David Laskowski

David Spencer Ewens

Frederick William Block

Timothy Lee Janssen

Mustafa Tekin Dokucu

Scott William Szepek

Christopher Eugene Long

Karl Joseph Minto

Timothy L Janssen

Systems and methods for automatic feedback control are provided. According to one embodiment of the disclosure, a method for automatic feedback control may commence with receiving high-level control references by a low-level controller communicatively coupled to a high-level controller via the network connection. The method may further include generating, by the low-level controller, low-level control references for a hardware asset based at least in part on the high-level control references. The method may continue with transferring control of the hardware asset to the low-level controller in response to a loss of the network connection. The method may further include adjusting the low-level control references by a low-level control mechanism associated with the low-level controller in response to the loss of the network connection.

Systems and methods for automatic feedback control in a distributed control system

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.

Systems and methods for controlling and monitoring power assets

Systems and methods for health monitoring and upgrade of a distributed controller are provided. According to one embodiment of the disclosure, a method for health monitoring and upgrade of a distributed controller may commence with receiving, by a lower level controller from a high-level controller, high-level control references. The method may further include generating low-level control references for a hardware asset based at least in part on the high-level control references. The method may include monitoring the network connection and detecting an error in the network connection. In response to the detection of the error in the network connection, a control of the hardware asset may be transferred to the low-level controller. The method may further include determining that the network connection has been restored. In response to the determination that the network connection has been restored, the control of the hardware asset may be transferred to the high-level controller.

Systems and methods for health monitoring and upgrade of a distributed controller

Exemplary embodiments described in this disclosure pertain to a system that includes a high-level controller coupled to a low-level controller in an arrangement that allows the high-level controller to cooperate with the low-level controller for controlling a power generation unit. The high-level controller generates supplementary signals and/or supplementary code that is provided to a surrogate controller. The surrogate controller uses the supplementary signals and/or supplementary code to generate control software that is provided to the low-level controller for controlling certain operational aspects of the power generation unit that cannot be independently controlled by the low-level controller. If the high-level controller is placed in an independent mode of operation for any reason, the surrogate controller continues to cooperate with the low-level controller for controlling various operations of the power generation unit that were being controlled by the low-level controller prior to the high-level controller entering the independent mode of operation.

Multi-level controller systems and methods for controlling a physical asset

Exemplary embodiments pertain to a system that can include a high-level controller coupled to a low-level controller for controlling a physical asset. In one exemplary implementation, the high-level controller executes a first finite state machine for controlling a power generation unit via a network. The low-level controller executes a second finite state machine that may have fewer states than the first finite state machine. The second finite state machine places the low-level controller in a default mode of operation for controlling the power generation unit under various conditions such as when the high-level controller is controlling the physical asset during a normal mode of operation; when the high-level controller is revising the first finite state machine; when the high-level controller is controlling the physical asset using a revised first finite state machine; and/or upon detecting a loss of communications between the high-level controller and the low-level controller.

Distributed finite state machine configuration for controlling a physical asset

Embodiments of the disclosure can relate to quantification of gas turbine inlet filter blockage. In one embodiment, a method can include receiving data associated with an inlet flow conditioning system associated with a turbine in a power plant. The method may further include determining an inlet flow associated with the turbine, based at least in part on operational data from the turbine. The method may further include determining an effective area of the inlet flow conditioning system, based at least in part on the inlet flow associated with the turbine. The method can further include determining a fouling rate of the inlet flow conditioning system, based at least in part on a difference between the effective area of the inlet flow conditioning system and a corresponding effective area of the inlet flow conditioning system determined at a prior operation. The method may further include identifying a corrective recommendation for the inlet flow conditioning system, based at least in part on the fouling rate and a performance target of the turbine.

Systems and methods for quantification of a gas turbine inlet filter blockage

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.

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Simpsonville, SC, United States of America

John Joseph Raffensperger