Power generating asset control in response to a grid event

Abstract

A method for controlling a power generating asset connected to an electrical grid. The method includes receiving, via a controller, an indication of a voltage-drop grid event occurring in the electrical grid. The method also includes, during recovery from the voltage-drop grid event, implementing, via the controller, a power and load optimization function. The power and load optimization function includes estimating a state of the power generating asset. The power and load optimization function also includes determining, via a supervisory control model programmed in the controller, a nominal command based, at least in part, on the estimated state. The power and load optimization function further includes predicting, via a nonlinear optimal control model programmed in the controller, an optimized command based, at least in part, on the estimated state and the nominal command. The optimized command is configured to satisfy one or more operational constraints during operation of the power generating asset. In addition, the power and load optimization function includes controlling, via the controller, the power generating asset based on the optimized command.

Description

701077-WO-1 / GECW-1287-PCTPOWER GENERATING ASSET CONTROL IN RESPONSE TO A GRID EVENTFIELD

[0001] The present disclosure relates in general to power generating assets, and more particularly to systems and methods for controlling a power generating asset in response to a voltage-drop grid event.BACKGROUND

[0002] Power generating assets take a variety of forms and rely on renewable and / or nonrenewable sources of energy. Those power generating assets relying on renewable sources of energy may generally be considered one of the cleanest, most environmentally friendly energy sources presently available. For example, wind turbines have gained increased attention in this regard. A modem wind turbine typically includes a tower, a generator, a gearbox, a nacelle, and one or more rotor blades. The nacelle includes a rotor coupled to the gearbox and to the generator. The rotor and the gearbox are mounted on a bedplate support frame located within the nacelle. The rotor blades capture kinetic energy' of wind using know n airfoil principles. The rotor blades transmit the kinetic energy in the form of rotational energy so as to turn a shaft coupling the rotor blades to the gearbox, or if the gearbox is not used, directly to the generator. The generator then converts the mechanical energy to electrical energy and the electrical energy may be transmitted to a converter and / or a transformer housed within the tower and subsequently deployed to a utility' grid. Modem wind power generation systems ty pically take the form of a wind farm having multiple wind turbine generators that are operable to supply power to a transmission system providing power to an electrical grid.

[0003] Wind turbines can be distinguished in two ty pes: fixed speed and variable speed turbines. Conventionally, variable speed wind turbines are controlled as current sources connected to an electrical grid. In other words, the variable speed wind turbines rely on a grid frequency^ detected by a phase locked loop (PLL) as a reference and inject a specified amount of current into the grid. The conventional current source control of the wind turbines is based on the assumptions that the grid voltage waveforms are fundamental voltage waveforms with fixed frequency and magnitude701077-WO-1 / GECW-1287-PCTand that the penetration of wind power into the grid is low enough so as to not cause disturbances to the grid voltage magnitude and frequency. Thus, the wind turbines simply inject the specified current into the grid based on the fundamental voltage waveforms.

[0004] Voltage-drop grid events, such as low-voltage ride through (LVRT) and / or multi-fault ride through (MFRT) events, produce large transient mechanical loads in the one or more components of the wind turbine. These events can reach large magnitudes that can damage components of the wind turbine. Furthermore, these events may violate operability limits of the wind turbine and / or cause tower vibrations and / or tower strikes that can result in disruption in power generation of the wind turbine (e.g., by causing the wind turbine to shut down). Accordingly, existing drivetrain designs rely on a drive train damper (DTD) for mechanical stability. The DTD produces oscillations in grid power after fault clearing which can cause non-compliance in either power overshoots or damping and cause voltage stability issues in very weak grids. In addition, more aggressive DTD gains to reduce mechanical loading can increase a likelihood for power oscillation non-compliance.

[0005] In view of the foregoing, the art is continuously seeking new and improved systems and methods for controlling a power generating asset in response to a voltage-drop grid event.BRIEF DESCRIPTION

[0006] Aspects and advantages of the invention will be set forth in part in the following description, or may be obvious from the description, or may be learned through practice of the invention.

[0007] In an aspect, the present disclosure is directed to a method for controlling a power generating asset connected to an electrical grid. The power generating asset has a power converter and a drivetrain with a generator. The method includes receiving, via a controller, an indication of a voltage-drop grid event occurring in the electrical grid. The method also includes, during recovery from the voltage-drop grid event, implementing, via the controller, a power and load optimization function. The power and load optimization function includes estimating a state of the power generating asset. The power and load optimization function also includes determining,701077-WO-1 / GECW-1287-PCTvia a supervisory control model programmed in the controller, a nominal command based, at least in part, on the estimated state. The power and load optimization function further includes predicting, via a nonlinear optimal control model programmed in the controller, an optimized command based, at least in part, on the estimated state and the nominal command. The optimized command is configured to satisfy one or more operational constraints during operation of the power generating asset. In addition, the power and load optimization function includes controlling, via the controller, the power generating asset based on the optimized command.

[0008] In another aspect, the present disclosure is directed to a power generating asset connected to an electrical grid. The power generating asset includes a generator, a power converter coupled to the generator, and a controller comprising at least one processor configured to perform a plurality of operations. The plurality of operations includes receiving, via a controller, an indication of a voltage-drop grid event occurring in the electrical grid. The plurality of operations further includes, during recovery from the voltage-drop grid event, implementing, via the controller, a power and load optimization function. The power and load optimization function includes estimating a state of the power generating asset. The power and load optimization function also includes determining, via a supervisory control model programmed in the controller, a nominal command based, at least in part, on the estimated state. The power and load optimization function further includes predicting, via a nonlinear optimal control model programmed in the controller, an optimized command based, at least in part, on the estimated state and the nominal command. The optimized command is configured to satisfy one or more operational constraints during operation of the power generating asset. In addition, the power and load optimization function includes controlling, via the controller, the power generating asset based on the optimized command.

[0009] These and other features, aspects and advantages of the present invention will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.701077-WO-1 / GECW-1287-PCTBRIEF DESCRIPTION OF THE DRAWINGS

[0010] A full and enabling disclosure of the present invention, including the best mode thereof, directed to one of ordinary skill in the art, is set forth in the specification, which makes reference to the appended figures, in which:

[0011] FIG. 1 illustrates a perspective view of an embodiment of a power generating asset configured as a wind turbine power system according to the present disclosure;

[0012] FIG. 2 illustrates a schematic diagram of an embodiment of an electrical system for use with a power generating asset configured as a wind turbine power system according to the present disclosure;

[0013] FIG. 3 illustrates a block diagram of an embodiment of a controller for use with a power generating asset according to the present disclosure;

[0014] FIG. 4 illustrates a simplified, schematic diagram of the electrical system of FIG. 2, particularly illustrating power flow during normal operations and during one or more grid events according to the present disclosure;

[0015] FIG. 5 illustrates a flow diagram of an embodiment of a method for controlling a power generating asset connected to an electrical grid according to the present disclosure;

[0016] FIG. 6 illustrates a schematic diagram of an embodiment of a power and load optimization function according to the present disclosure;

[0017] FIG. 7 illustrates a schematic diagram of an embodiment of the nonlinear optimal control model comprising nonlinear model predictive control according to the present disclosure;

[0018] FIG. 8 illustrates a schematic diagram of another embodiment of the nonlinear optimal control model having nonlinear model predictive control according to the present disclosure;

[0019] FIG. 9 illustrates a schematic diagram of an embodiment of the nonlinear optimal control model comprising linear model predictive control according to the present disclosure; and

[0020] FIG. 10 illustrates a schematic diagram of an embodiment of the nonlinear optimal control model comprising parametric control according to the present disclosure.701077-WO-1 / GECW-1287-PCT

[0021] Repeat use of reference characters in the present specification and drawings is intended to represent the same or analogous features or elements of the present invention.DETAILED DESCRIPTION

[0022] Reference now will be made in detail to embodiments of the invention, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the invention, not limitation of the invention. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope or spirit of the invention. For instance, features illustrated or described as part of an embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present invention covers such modifications and variations as come within the scope of the appended claims and their equivalents.

[0023] As used herein, the terms “first”, “second”, and “third” may be used interchangeably to distinguish one component from another and are not intended to signify location or importance of the individual components.

[0024] The terms “coupled,” “fixed,” “attached to,” and the like refer to both direct coupling, fixing, or attaching, as well as indirect coupling, fixing, or attaching through one or more intermediate components or features, unless otherwise specified herein.

[0025] Approximating language, as used herein throughout the specification and claims, is applied to modify any quantitative representation that could permissibly vary without resulting in a change in the basic function to which it is related.Accordingly, a value modified by a term or terms, such as “about”, “approximately”, and “substantially”, are not to be limited to the precise value specified. In at least some instances, the approximating language may correspond to the precision of an instrument for measuring the value, or the precision of the methods or machines for constructing or manufacturing the components and / or systems. For example, the approximating language may refer to being within a 10 percent margin.

[0026] Here and throughout the specification and claims, range limitations are combined and interchanged, such ranges are identified and include all the sub-ranges701077-WO-1 / GECW-1287-PCTcontained therein unless context or language indicates otherwise. For example, all ranges disclosed herein are inclusive of the endpoints, and the endpoints are independently combinable with each other.

[0027] Voltage-drop grid events, such as low-voltage ride through (LVRT) and / or multi-fault ride through (MFRT) events, produce large transient mechanical loads in one or more components of a wind turbine that can damage components of the w ind turbine. Accordingly, existing drivetrain designs for wind turbine power systems may rely on a slip coupling to meet LVRT / MFRT requirements and for protection of the components of the w ind turbine, such as the gearbox. How ever, the slip coupling can wear out quickly and can be expensive to replace. Moreover, existing grid codes require power generating assets to recover active power to pre-fault power levels following grid faults and to avoid excessive deviations from the pre-fault power levels. Certain control functions used to manage mechanical loading on the wind turbine may cause deviations (e.g., from pre-grid-disturbance) in power during recovery from a grid event. Such deviations can lead to non-compliance with the different grid codes or increase voltage stability risks if the power deviations are excessively large and the grid is weak.

[0028] Accordingly, the present disclosure is directed to systems and methods for controlling a pow er generating asset, such as a wind turbine, connected to an electrical grid that utilizes a power and load optimization function to predict an optimized command configured to satisfy one or more operational constraints while operating the power generating asset during recovery from a voltage-drop grid event. In an embodiment, the pow er and load optimization function can predict the optimized command by using a nonlinear optimal control model. The nonlinear optimal control model uses optimal control theory to induce or stop specific states of a dynamic system. Thus, systems and methods of the present disclosure proactively satisfy the operational constraint(s) instead of reacting to constraint violations, thereby reducing a likelihood of excessive loading or power oscillation non-compliance during recovery from the voltage-drop grid event.

[0029] Referring now to the drawings, FIG. 1 illustrates a perspective view' of an embodiment of a pow er generating asset 100 according to the present disclosure. As shown, the pow er generating asset 100 may be configured as a wind turbine 102. In701077-WO-1 / GECW-1287-PCTan additional embodiment, the power generating asset 100 may. for example, be configured as a hydroelectric plant, a fossil fuel generator, and / or a hybrid power generating asset.

[0030] When configured as a wind turbine 102, the power generating asset 100 may generally include a tower 104 extending from a support surface 103, a nacelle 106 mounted on the tower 104, and a rotor 108 coupled to the nacelle 106. The rotor 108 includes a rotatable hub 110 and at least one rotor blade 112 coupled to and extending outwardly from the hub 110. For example, in the illustrated embodiment, the rotor 108 includes three rotor blades 112. However, in an alternative embodiment, the rotor 108 may include more or less than three rotor blades 112. Each rotor blade 112 may be spaced about the hub 110 to facilitate rotating the rotor 108 to enable kinetic energy to be transferred from the wind into usable mechanical energy, and subsequently, electrical energy. For instance, the hub 110 may be rotatably coupled to an electric generator 118 (FIG. 2) of an electrical system 200 (FIG. 2) positioned within the nacelle 106 to permit electrical energy to be produced.

[0031] The wind turbine 102 may also include a controller 120 centralized within the nacelle 106. However, in other embodiments, the controller 120 may be located within any other component of the wind turbine 102 or at a location outside the wind turbine 102. Further, the controller 120 may be communicatively coupled to any number of the components of the wind turbine 102 in order to control the components. As such, the controller 120 may include a computer or other suitable processing unit. Thus, in several embodiments, the controller 120 may include suitable computer-readable instructions that, when implemented, configure the controller 120 to perform various different functions, such as receiving, transmitting and / or executing wind turbine control signals.

[0032] Furthermore, as depicted in FIG. 1, in an embodiment, the power generating asset 100 may include at least one operational sensor 122. The operational sensor(s) 122 may be configured to detect a performance of the power generating asset 100, e.g., in response to the environmental condition. In an embodiment, the operational sensor(s) 122 may be configured to monitor a plurality of electrical conditions, such as slip, stator voltage and current, rotor voltage and current, line-side voltage and current, DC-link charge and / or any other electrical condition of the power701077-WO-1 / GECW-1287-PCTgenerating asset 100.

[0033] It should also be appreciated that, as used herein, the term “monitor” and variations thereof indicates that the various sensors of the power generating asset 100 may be configured to provide a direct measurement of the parameters being monitored or an indirect measurement of such parameters. Thus, the sensor(s) 122 described herein may, for example, be used to generate signals relating to the parameter being monitored, which can then be utilized by the controller 120 to determine a condition or response of the power generating asset 100.

[0034] Referring now to FIG. 2, an embodiment of an electrical system 200 of the power generating asset 100 is illustrated. As shown, the generator 118 may be coupled to the rotor 108 for producing electrical power from the rotational energy generated by the rotor 108. Accordingly, in an embodiment, the electrical system 200 may include various components for converting the kinetic energy of the rotor 108 into an electrical output in an acceptable form to an electrical grid 202 via grid bus 204. For example, in an embodiment, the generator 118 may be a double-fed induction generator (DFIG) having a stator 206 and a generator rotor 208. The generator 118 may be coupled to a stator bus 210 and a power converter 220 via a rotor bus 212. In such a configuration, the stator bus 210 may provide an output multiphase pow er (e.g., three-phase power) from a stator of the generator 118, and the rotor bus 212 may provide an output multiphase power (e.g., three-phase power) of the generator rotor 208 of the generator 118. Additionally, the generator 118 may be coupled via the rotor bus 212 to a rotor side converter 222. The rotor side converter 222 may be coupled to a line-side converter 224 w hich, in turn, may be coupled to a line-side bus 214.

[0035] In an embodiment, the rotor side converter 222 and the line-side converter 224 may be configured for normal operating mode in a three-phase, pulse width modulation (PWM) arrangement using insulated gate bipolar transistors (IGBTs) Other suitable switching devices may be used, such as insulated gate commuted thyristors, MOSFETs, bipolar transistors, silicone-controlled rectifiers, and / or other suitable switching devices. Furthermore, as shown, the rotor side converter 222 and the line-side converter 224 may be coupled via a DC link 226 across a DC link capacitor 228.701077-WO-1 / GECW-1287-PCT

[0036] In an embodiment, the power converter 220 may be coupled to the controller 120 configured as a converter controller 230 to control the operation of the power converter 220. For example, the converter controller 230 may send control commands to the rotor side converter 222 and the line-side converter 224 to control the modulation of switching elements used in the power converter 220 to establish a desired generator torque setpoint and / or power output.

[0037] As further depicted in FIG. 2, the electrical system 200 may, in an embodiment, include a transformer 216 coupling the power generating asset of 100 to the electrical grid 202. The transformer 216 may, in an embodiment, be a three- winding transformer which includes a high voltage (e.g., greater than 12 KVAC) primary winding 217. The high voltage primary winding 217 may be coupled to the electrical grid 179. The transformer 216 may also include a medium voltage (e.g., 6 KVAC) secondary winding 218 coupled to the stator bus 210 and a low voltage (e.g., 575 VAC, 690 VAC, etc.) auxiliary winding 219 coupled to the line bus 214. It should be appreciated that the transformer 216 can be a three- winding transformer as depicted, or alternatively, may be a two-winding transformer having only the primary winding 217 and the secondary winding 218; may be a four-winding transformer having the primary winding 217, the secondary winding 218, the auxiliary winding 219, and an additional auxiliary winding; or may have any other suitable number of windings.

[0038] In an embodiment, the electrical system 200 may include various protective features (e.g., circuit breakers, fuses, contactors, and other devices) to control and / or protect the various components of the electrical system 200. For example, the electrical system 200 may, in an embodiment, include a grid circuit breaker 232, a stator bus circuit breaker 234, and / or a line bus circuit breaker 236. The circuit breakers) 232, 234, 236 of the electrical system 200 may connect or disconnect corresponding components of the electrical system 200 when a condition of the electrical system 200 approaches a threshold (e.g., a current threshold and / or an operational threshold) of the electrical system 200.

[0039] Referring now to FIG. 3, a block diagram of an embodiment of suitable components that may be included within a controller 300 of the power generating asset 100, such as the wind turbine 102, is illustrated. For example, as shown, the701077-WO-1 / GECW-1287-PCTcontroller 300 may be the turbine controller 120 or the converter controller 230. Further, as shown, the controller 300 includes one or more processor(s) 302 and associated memory device(s) 304 configured to perform a variety ofcomputer-implemented functions (e.g., performing the methods, steps, calculations and the like and storing relevant data as disclosed herein). Additionally, the controller 300 may also include a communications module 306 to facilitate communications between the controller 300, and the various components of the power generating asset 100. Further, the communications module 306 may include a sensor interface 308 (e.g., one or more analog-to-digital converters) to permit signals transmitted from the sensor(s) 122 to be converted into signals that can be understood and processed by the processors 302. It should be appreciated that the sensor(s) 122 may be communicatively coupled to the communications module 306 using any suitable means. For example, the sensor(s) 122 may be coupled to the sensor interface 308 via a wired connection. However, in other embodiments, the sensor(s) 122 may be coupled to the sensor interface 308 via a wireless connection, such as by using any suitable wireless communications protocol known in the art.

[0040] As used herein, the term “processor” refers not only to integrated circuits referred to in the art as being included in a computer, but also refers to a controller, a microcontroller, a microcomputer, a programmable logic controller (PLC), an application specific integrated circuit, and other programmable circuits. Additionally, the memory device(s) 304 may generally include memory element(s) including, but not limited to, computer readable medium (e.g., random access memory (RAM)), computer readable non-volatile medium (e.g., a flash memory), a floppy disk, a compact disc-read only memory (CD-ROM), a magneto-optical disk (MOD), a digital versatile disc (DVD) and / or other suitable memory elements. Such memory device(s) 304 may generally be configured to store suitable computer-readable instructions that, when implemented by the processor(s) 302, configure the controller 300 to perform various functions as described herein, as w ell as various other suitablecomputer-implemented functions.

[0041] Referring now to FIG. 4, a simplified, schematic diagram of the electrical system 200 of FIG. 2 is illustrated, particularly illustrating power flow during normal operations and during one or more grid events according to the present disclosure.701077-WO-1 / GECW-1287-PCTMore specifically, as shown, the power flow during normal operations is represented by the solid arrows throughout the system 200, whereas the power flow during the grid event(s) is represented by the dotted arrows within the dotted boxes throughout the system 200. Moreover, as shown, the power flow at the output of the system 200 (i.e., Pt and PT in FIG. 4) reflects the grid power / net power output of the system 200. Further, in an embodiment, the generator power is equal to the electric torque on the generator 118 multiplied by the operating speed, which is reflected as power flow through the stator and rotor windings of the generator 118. Most of the generator power flows through the stator (i.e., Ps in FIG. 4) during normal and grid-fault conditions.

[0042] Referring now to FIG. 5, a flow diagram of an embodiment of a method 400 for controlling the power generating asset 100, particularly during a voltage-drop grid event, is presented according to the present disclosure. The method 400 may be implemented using, for instance, the controller 300 of the present disclosure discussed above with references to FIGS. 2-4. FIG. 5 depicts steps performed in a particular order for purposes of illustration and discussion. Those of ordinary skill in the art. using the disclosures provided herein, will understand that various steps of the method 400, or any of the methods disclosed herein, may be adapted, modified, rearranged, performed simultaneously, or modified in various ways without deviating from the scope of the present disclosure.

[0043] As shown at (402), the method (400) includes receiving, via the controller 300, an indication of the voltage-drop grid event(s) occurring in the electrical grid 202. For instance, a grid voltage-drop event, as described herein, is generally associated with a dip in voltage in the electrical grid 202 that can be detected. In particular, the voltage-drop grid event can be an LVRT event (i.e., a single LVRT event occurring in the electrical grid 202) or an MFRT event (i.e., one LVRT event following by one or more subsequent LVRT events occurring in the electrical grid 202). In some embodiments, the voltage-drop grid event(s) can be detected based at least in part on data received from various sensors 122 associated with the wind turbine power system 200.

[0044] During recovery from the voltage-drop grid event(s), as shown at (404), the method 400 includes implementing, via the controller 300, a power and load701077-WO-1 / GECW-1287-PCToptimization function 406. In particular, as shown at (408), the power and load optimization function 406 includes, at least, estimating a state of the wind turbine 102. As shown at (410). the power and load optimization function 406 includes determining, via a supervisory control model 504 programmed in the controller 300, a nominal command based, at least in part on, the estimated state of the wind turbine 102. Moreover, as shown at (412), the power and load optimization function 406 includes predicting, via a nonlinear optimal control model 512 programmed in the controller 300, an optimized command based, at least in part on, the estimated state and the nominal command. The optimized command is configured to satisfy one or more operational constraints during operation of the wind turbine 102. Further, as shown at (414), the method 400 includes controlling, via the controller 300, the wind turbine 102 based on the optimized command. Thus, the power and load optimization function 406 is configured to predict the optimized command that controls operation of the wind turbine 102 such that the operational constraint(s) are satisfied. As such, the power and load optimization function 406 can prevent excessive loads and power oscillation non-compliance and can maximize performance of the wind turbine 102 during recovery from the voltage-drop grid event(s).

[0045] The method 400 of FIG. 5 can be better understood with reference to FIGS. 6-10. In particular, FIG. 6 illustrates a schematic diagram of an embodiment of the power and load optimization function 406 according to the present disclosure. FIG. 7 illustrates a schematic diagram of an embodiment of the nonlinear optimal control model 512 including non-linear model predictive control according to the present disclosure. FIG. 8 illustrates a schematic diagram of another embodiment of the nonlinear optimal control model 512 including non-linear model predictive control according to the present disclosure. FIG. 9 illustrates a schematic diagram of an embodiment of the nonlinear optimal control model 512 including linear model predictive control according to the present disclosure. FIG. 10 illustrates a schematic diagram of an embodiment of the nonlinear optimal control model 512 including parametric control according to the present disclosure.

[0046] Referring particularly to FIG. 6, the controller 300 activates the power and load optimization function 406 during the recovery from the voltage-drop grid event(s). That is, the controller 300 activates the power and load optimization701077-WO-1 / GECW-1287-PCTfunction 406 at a start of the recovery and maintains activation of the power and load optimization function 406 until an end of the recovery. As an example, the power and load optimization function 406 may be activated for a predetermined period of time after the voltage-drop grid event ends (e.g., 1-10 seconds). As another example, the power and load optimization function 406 may be activated until one or more operating parameters (e.g., a generator torque, a generator speed, a power output, etc.) reach (or are within a threshold value of) a steady-state (or pre-grid event) value.

[0047] When the power and load optimization function 406 is activated, the power and load optimization function 406 predicts the optimized command(s) for controlling operation of the wind turbine 102 at a next time step. That is, recovery may be specified as an N-step finite horizon. In some embodiments, time steps of the N-step finite horizon may be determined based on dynamics components (such as natural frequencies) that result from being controlled during recovery. In additional or alternative embodiments, the time steps may, for example, account for delays in communication between the various components (e.g., sensor 122, the state estimator, the controller, etc.) within the wind turbine 102.

[0048] Further, the control of the wind turbine 102 may be continually optimized. For example, by controlling the wind turbine 102 based on the optimized command(s), the power and load optimization function 406 may be able to determine new optimized command(s). That is, the power and load optimization function 406 may continually determine optimized command(s) for each time step in the N-step finite horizon (i.e., until an end of the recovery).

[0049] To predict the optimized command(s), the power and load optimization function 406 estimates a state of the wind turbine 102 at a current time step. For example, as shown in FIG. 6, the power and load optimization function 406 may include a state estimator 500 that receives a feedback signal 501 specifying data associated with operation of the wind turbine 102 and outputs an estimated state 502 of the wind turbine 102. As used herein, a “state’' is a set of variables that defines a dynamic system and predicts an output of the dynamic system at future time steps.

[0050] The data associated with operation of the wind turbine 102 can be collected by the sensor(s) 122. The collected data may include operational parameters of the wind turbine 102 defining the state of the wind turbine 102. By way of701077-WO-1 / GECW-1287-PCTexample, the collected data can include sensed values for various operational parameters, such as rotor speed, blade pitch, nacelle yaw, actual power output, generator speed, etc. The sensed values for these operational parameters can be utilized to calculate or determine (e g., via model-based estimation) values for other operational parameters associated with the asset (e.g., mechanical loads on various components (such as the tower 104, the blade(s) 112, the hub 110, the gearbox etc.), component stresses and strains, expected power output, etc.). The calculated values can be included in the data associated with the operation of the wind turbine 102.

[0051] Moreover, as shown in FIG. 6, the supervisory control model 504 receives a plurality of inputs. In embodiments, the plurality of inputs for the supervisory control model 504 may include the feedback signal 501, the estimated state 502 of the wind turbine 102, and / or any other suitable input. The supervisory control model 504 is configured to output the nominal command(s) 506. The nominal command(s) 506 are provided to control operation of the wind turbine 102 and / or components thereof at the current time step. In embodiments, the nominal command(s) 506 may be at least one of a generator torque, a current, and a blade pitch, and / or any other suitable control parameter. In some embodiments, the supervisory control model 504 may access or derive the nominal command(s) 506 from the data specified in the feedback signal 501 and / or the estimated state 502 of the wind turbine 102 (e.g., using known calculation techniques).

[0052] Referring still to FIG. 6, in certain embodiments, as shown, the supervisory’ control model 504 may be further configured to output one or more reference operating parameters 508 for the wind turbine 102 at the current time step. In some embodiments, the reference operating parameter(s) 508 may be fixed (e.g., steady-state (or pre-grid event) value(s) of the operating parameter(s)). In other embodiments, the reference operating parameter(s) 508 may be variable (e.g., the reference operating parameter(s) may be determined as a function of the finite horizon). Further, in some embodiments, as shown, the supervisory control model 504 may be further configured to output a respective weight 510 associated with each reference operating parameter 508. In such embodiments, the weight(s) 510 may be configured to account for operating conditions, such as wind speed, and fault severity (e.g., defined by a number of voltage dips, a duration of each voltage dip, and / or an701077-WO-1 / GECW-1287-PCTinterval between consecutive voltage dips), which can improve performance of the power and load optimization function 406.

[0053] As shown, the nonlinear optimal control model 512 receives a plurality of inputs. More specifically, the nonlinear optimal control model 512 receives the estimated state 502 of the wind turbine 102 from the state estimator 500 and the nominal command(s) 506 from the supervisory control model 504. In some embodiments, the nonlinear optimal control model 512 may receive the reference operating parameter(s) 508 from the supervisory control model 504. In such embodiments, the nonlinear optimal control model 512 may further receive the weight(s) 510 from the supervisory control model 504.

[0054] Thus, as shown, the nonlinear optimal control model 512 is configured to define an optimization function and output the optimized command(s) 514 to the controller 300. The controller 300 then controls operation of the wind turbine 102 based on the optimized command(s) 514. More specifically, the controller 300 actuates components to achieve the optimized command(s) 514, which results in operating the wind turbine 102 within the operational constraint(s) for a next time step in the N-step finite horizon. Each operational constraint may specify upper and / or lower limits for the corresponding operating parameter. The upper and / or the lower limits may be absolute values or they may be values that may change based on realtime data analytics (e.g., so as to prevent component damage and permit operation of the wind turbine that satisfies grid codes).

[0055] Referring now to FIG. 7, in some embodiments, the nonlinear optimal control model 512 includes non-linear model predictive control 513. In general, the nonlinear optimal control model 512 is described herein with reference to one optimized command 514 being a generator torque. However, it should be appreciated that the nonlinear optimal control model 512 may be implemented with additional optimized commands 514 being a current, a blade pitch, the generator torque, and / or any other suitable parameter or combination thereof provided for controlling operation of the wind turbine 102.

[0056] In such embodiments, the optimization function may include a cost function J. The cost function J may be configured to minimize a deviation between the optimized command 514 and the nominal command 506 while satisfying the701077-WO-1 / GECW-1287-PCToperational constraint(s) of the wind turbine. In such embodiments, the operational constraints may include, but are not limited to, mechanical load constraints, (e.g., gearbox load constraints, tower load constraints, blade load constraints, hub load constraints, etc.) generator torque constraints, and correction in generator torque constraints. However, it should be appreciated that the operational constraints may include fewer or additional constraints (including, but not limited to, a maximum recovery duration, a settling time for the transient response to cease, a power output, a generator (or rotor) speed, or a combination thereof). In an embodiment, the cost function J may be Equation (1) provided below:N-lmin / = w0(dTfe)2fc=OS.t. Xk+1= f(xk, Tk)yk = g(xk, Tk)T^k = Tk+ ATkMmin — — ^max ^min — ^Tk_ Tmax^min — T < ^maxwhere w0is a weight 510,Mkis a mechanical load, such as a gearbox load, a tower load, a hub load, a blade load, etc. (i.e., an operational parameter),Mmaxand Mmtn are constraints on the mechanical load,Tkis a generator torque (i.e., the optimized command),Avkis a correction in the generator torque,and imaxare constraints on the change in the generator torque,Tminand Tmaxare constraints on the generator torque,Tkis the nominal generator torque (i.e., the nominal command), andxk+1= f(xk, rk) and yk= g(xklrk) are nonlinear dynamic equations representing operation of the drive train and the wind turbine 102.

[0057] Still referring to FIG. 7, in additional or alternative embodiments, the cost function J may be configured to further minimize respective deviations between701077-WO-1 / GECW-1287-PCTrespective predicted operating parameters (e.g., derived from or included in the estimated state 502) and respective reference operating parameters 508 (e.g., output via the supervisory control model 504). In such embodiments, the operating parameters may include, but are not limited to, a power output and a generator (or rotor) speed and / or any other suitable operating parameter. For example, the cost function J may be Equation (2) provided below:N-l Z 2 2 W0(dTk)2+ - (Pre / ) + W2(Pk- Pref)k=0S.t. Xk+1= f xk, Tk)yk = g,xk, Tk>) (2) T^k = Tk+ Avkmin —k—maxtmin — ^Tk_ Amax^min — — ^max' P min < —JP k < —JP max / IP min < —k A P k < — “ A‘PmaxC^min — — ^maxwhere wltw2are weights 510,ωrefis a reference speed of the generator or the rotor,Pref is a reference power output.Pkis a predicted power output by the wind turbine 102,Pmaxand Pminare operational constraints on the power output,APkis a rate of change of the power output by the wind turbine 102.APmaxand APminare operational constraints on the rate of change of the power output,ωkis a predicted speed of the generator or the rotor, andand ωminare operation constraints on the speed of the generator or the rotor.

[0058] Still referring to FIG. 7, in additional or alternative embodiments, the cost701077-WO-1 / GECW-1287-PCTfunction J may include a penalty on a magnitude of slack variables. The slack variables may be combined with the operational constraint(s) to permit some violation of the operational constraint(s). For example, the cost function J may be Equation (3) provided below:N-l Z 2 2 w0(dTk)2+ - rore / ) + iv2(fk- Pref) + W3£^k=0+ W4Eks.t. xk+1= f(xk, Tk) (3) Vk = 9 xk, Tk)^k = Tk+ dTkM-min ~ £kMkMmax+£k^min£k — ^k — ^max +£k£min£k —£k— ~^max +£kPmin£k — Pk — Pmax T£kAPmin~£k — APk< APmax+ Sk^min£k — ^k — ^max +£k£k 0where w3, w4are weights 510,Ekis a slack variable

[0059] In additional or alternative embodiments, the grid code may specify a damping coefficient for recovery of power output and mechanical load after the grid event(s). In such embodiments, the gearbox load constraints may be modulated according to Equation (4) below:+ (1 - a)e~bt) < Mk< Mmax(a + (1 - a)e~bt} (4) Pmin(a + (1 - a)e~bt} < Pk< Pmcix(a + (1 - a)e-bt)where a and b are coefficients determined as a function of the specified damping701077-WO-1 / GECW-1287-PCTcoefficient.

[0060] Referring now to FIG. 8, in additional or alternative embodiments, the nonlinear optimal control model 512 can output an additional optimized command 515. In general, the nonlinear optimal control model 512 is described herein with reference to the additional optimized command 515 being a blade pitch. However, it should be appreciated that the nonlinear optimal control model 512 may be implemented with the additional optimized command 515 being a current or any other suitable parameter provided for controlling operation of the wind turbine 102.Further, it should be appreciated that the nonlinear optimal control model 512 may be implemented to output one or more additional optimized commands 514.

[0061] In such embodiments, the cost function J may be configured to minimize respective deviations between a plurality of optimized commands 514, 515 and corresponding nominal commands 506 while satisfying the operational constraint(s) of the wind turbine 102. In such embodiments, the operational constraints may include, but are not limited to, gearbox load constraints, generator torque constraints, blade pitch constraints, constraints on blade pitch correction. and constraints on generator torque correction. However, it should be appreciated that the operational constraints may include fewer or additional constraints (including, but not limited to, a maximum recovery duration, a settling time in which the transient response should cease, a power output, a generator (or rotor) speed, tower vibration, blade deflection, or a combination thereof). In an embodiment, the cost function J may be Equation (5) provided below:N-lminj = y iv0(zlTfe)2+ wfyzie)2fc = 0s.t. xk+1= f(xk, Tk, 0k)Vk = 9 xk, i:k, 6k)k= Tk+ Ark(5)9k= 0k+ A0kmin — —max^min Tk< Amax^min — ^k — ^max701077-WO-1 / GECW-1287-PCTA min — ^0k — 0max^min — — &maxwhere A0kis a correction in the blade pitch,0minand 0maxare constraints on the blade pitch,0kis the optimized pitch control, and0kis the nominal pitch control.

[0062] The solution to the optimization function may include finding one or more corrections to the nominal command(s) 506 that minimize(s) the output of the cost function J while satisfying the operational constraint(s). The nonlinear optimal control model 512 may include any type of algorithm, program, or software that will determine values that result in the minimization of the output of the cost function J. The nonlinear optimal control model 512 may then determine the optimized command(s) 514, 515 by combining the nominal command(s) 506 and the correction(s). as shown above. In some embodiments, the optimized command(s) 514, 515 may be an output in the form of a time series of values (or sets of values) of the optimized command(s) 514, 515. In such embodiments, the nonlinear optimal control model 512 may be configured to select a first value (or first set of values) in the time series to be the optimized command(s) 514, 515. Further, the time series may be provided as feedback 517 to the nonlinear optimal control model 512, as shown.

[0063] As such, through minimizing a cost function, optimized commands may be achieved to operate of the wind turbine within operational constraint(s). Further, customization can be achieved by scheduling weight(s) according to some operational parameter, such as wind speed, and / or fault severity.

[0064] Referring now to FIG. 9, in some embodiments, the nonlinear optimal control model 512 includes linear model predictive control. In general, the nonlinear optimal control model 512 is described herein with reference to the optimized command 514 being a generator torque. However, it should be appreciated that the nonlinear optimal control model 512 may be implemented with the optimized command 514 being a current, a blade pitch, or any other suitable parameter provided for controlling operation of the wind turbine 102.701077-WO-1 / GECW-1287-PCT

[0065] In the embodiment shown in FIG. 9, the nonlinear optimal control model 512 includes a linearization model 516 configured to create a linear approximation of the estimated state of the wind turbine 102. That is. the linearization model 516 receives the estimated state of the wind turbine 102 (e.g., via the state estimator 500) as input and outputs a linearized system 518 configured to estimate the state of the wind turbine 102. Further, the power and load optimization function 406 includes a quadratic programming model 520 that receives a plurality of inputs. The plurality of inputs may include the linearized system 518, the nominal command 506, the reference operating parameters 508, the weights 510, or a combination thereof. The quadratic programming model 520 is configured to output the optimized command 514 (e.g.. by solving the linearized system according to known techniques). Utilizing the linearization model 516 and the quadratic programming model 520 of linear model predictive control can reduce computational resources consumed in determining the optimized command 514 as compared to nonlinear model predictive control 513.

[0066] In such embodiments, the optimization function may include a cost function J configured to minimize the deviation between the optimized command 514 and the nominal command 506 while satisfying operational constraint(s). As mentioned above, the operational constraints may include, but are not limited to, gearbox load constraints, generator torque constraints, and correction in generator torque constraints. In an embodiment, the cost function J may be Equation (6) provided below:

[0067] IV— 1min / = > w0(dTAT / •k)2k=0s.t. xk+1= Akxk+ BkTkyk= Ckxk+ Dkrk^k = Tk+ rk6)M1 Vlmi ■n < —1Mlkr < — M maxmin — Tk <maxT'min — T'k — ^max701077-WO-1 / GECW-1287-PCTwhere xk+1and ykare the linearized system 518,A, B, C, and D are coefficients defining the estimated state of the wind turbine 102.

[0068] As mentioned above, the solution to the optimization function may include finding a correction to the nominal command 506 that minimizes the output of the cost function J while satisfying the operational constraint(s). As such, the nonlinear optimal control model 512 may then determine the optimized command 514 by combining the nominal command 506 and the correction, as shown above.

[0069] As mentioned above, in some embodiments, the optimized command 514 may be an output in the form of a time series of values (i.e., a trajectory) of the control torque. In such embodiments, the time series may be provided as feedback 517 to the linearization model 516 and / or the quadratic programming model 520, as shown.

[0070] Referring now to FIG. 10, in some embodiments, the nonlinear optimal control model 512 includes parametric control 524. In general, the nonlinear optimal control model 512 is described herein with reference to the optimized command 514 being a generator torque. However, it should be appreciated that the nonlinear optimal control model 512 may be implemented w ith the optimized command 514 being a current, a blade pitch, or any other suitable parameter provided for controlling operation of the wind turbine 102.

[0071] In the embodiment shown in FIG. 10, the nonlinear optimal control model 512 includes a prediction model 522 that receives the estimated state 502 of the wind turbine 102 (e.g., via the state estimator 500) as an input. The prediction model 522 is configured to predict a peak generator torque Mpeakwithin 1 / f seconds (e.g., by¬ solving xk+1= f(xk,k) according to know n techniques), where f is a first torsional frequency of a low-speed shaft (e.g., a rotor shaft). The nonlinear optimal control model 512 may be configured to then determine the optimized command 514 according to the parametric control 524 of Equation (7) below:= Tk4- AT0If Mpeak — Mmax& tpeak — 0.5 f.701077-WO-1 / GECW-1287-PCT21rfc= (-aiMpeafe+ a2) sin(27r / t); (7) else-. Ak= 0where and a2are coefficients determined, for example, as a function of Mmaxand an adjustable controller gain,tpeakis a timeat which Mpeakoccurs, andZ1T0is a first value of Ark.

[0072] In such an embodiment, the nominal generator torque Tkmay be determined (e.g., via the supervisory control model 504) according to Equation (8) below:„ > Pref (8)i k~

[0073] Through parametric control, control parameters may be updated or maintained so as to operate the wind turbine within operational constraint(s). Utilizing parametric control can further reduce computational resources consumed in determining the optimized command 514 as compared to linear model predictive control.

[0074] Furthermore, the skilled artisan will recognize the interchangeability' of various features from different embodiments. Similarly, the various method steps and features described, as well as other known equivalents for each such methods and feature, can be mixed and matched by one of ordinary' skill in this art to construct additional systems and techniques in accordance with principles of this disclosure. Of course, it is to be understood that not necessarily all such objects or advantages described above may be achieved in accordance with any particular embodiment. Thus, for example, those skilled in the art will recognize that the systems and techniques described herein may be embodied or carried out in a manner that achieves or optimizes one advantage or group of advantages as taught herein without necessarily achieving other objects or advantages as may be taught or suggested herein.

[0075] Further aspects of the invention are provided by the subject matter of the following clauses:701077-WO-1 / GECW-1287-PCT

[0076] A method for controlling a power generating asset connected to an electrical grid, the power generating asset having a power converter and a drivetrain with a generator, the method comprising: receiving, via a controller, an indication of a voltage-drop grid event occurring in the electrical grid; during recover}’ from the voltage-drop grid event, implementing, via the controller, a power and load optimization function, the power and load optimization function comprising: estimating a state of the power generating asset; determining, via a supervisory control model programmed in the controller, a nominal command based, at least in part, on the estimated state; and predicting, via a nonlinear optimal control model programmed in the controller, an optimized command based, at least in part, on the estimated state and the nominal command, the optimized command configured to satisfy one or more operational constraints during operation of the power generating asset; and controlling, via the controller, the power generating asset based on the optimized command.

[0077] The method of any preceding clause, wherein the voltage-drop grid event comprises one of a low-voltage ride through event (LVRT) or a multi-fault ride through (MFRT) event.

[0078] The method of any preceding clause, wherein the optimized command comprises at least one of a generator torque command, a current command, or a blade pitch command.

[0079] The method of any preceding clause, wherein the optimized command is predicted based further on at least one of a reference operating parameter and a weight to the nonlinear optimal control model.

[0080] The method of any preceding clause, further comprising determining the reference operating parameter and the weight based on the estimated state and data associated with operation of the power generating asset.

[0081] The method of any preceding clause, wherein predicting, via the nonlinear optimal control model programmed in the controller, the optimized command based, at least in part, on the estimated state and the nominal command further comprises: outputting, via the nonlinear optimal control model, a time series of values for the optimized command, the time series having a finite horizon; and selecting a first value in the time series to be the optimized command.701077-WO-1 / GECW-1287-PCT

[0082] The method of any preceding clause, wherein the power and load optimization function further comprises providing the time series as feedback to the nonlinear optimal control model.

[0083] The method of any preceding clause, wherein the power and load optimization function further comprises: determining, via the supervisory' control model, an additional nominal command based, at least in part, on the estimated state; and predicting, via the nonlinear optimal control model, an additional optimized command based, at least in part, on the estimated state and the additional nominal command.

[0084] The method of any preceding clause, wherein the optimized command comprises one of a torque command, a current command, or a blade pitch command, and the additional optimized command being another of the torque command, the current command, or the blade pitch command.

[0085] The method of any preceding clause, wherein the power and load optimization function further comprises: outputting, via the nonlinear optimal control model, a time series of a set of values for the optimized command and the additional optimized command, the time series having a finite horizon, wherein the nonlinear optimal control model includes nonlinear model predictive control; and selecting a first set in the time series to be the optimized command and the additional optimized command.

[0086] The method of any preceding clause, wherein the power and load optimization function further comprises providing the time series as feedback to the nonlinear optimal control model.

[0087] The method of any preceding clause, wherein the nonlinear optimal control model is configured to predict the optimized command by minimizing a cost function while satisfying the one or more operational constraints.

[0088] The method of any preceding clause, wherein the one or more operational constraints comprise at least one of a component load constraint, a component torque constraint, and a correction in the component torque constraint.

[0089] A power generating asset connected to an electrical grid, the power generating asset comprising: a generator; a power converter coupled to the generator; and a controller comprising at least one processor configured to perform a plurality of701077-WO-1 / GECW-1287-PCToperations, the plurality of operations comprising: receiving, via a controller, an indication of a voltage-drop grid event occurring in the electrical grid; during recovery from the voltage-drop grid event, implementing, via the controller, a power and load optimization function, the power and load optimization function comprising: estimating a state of the power generating asset; determining, via a supervisory' control model programmed in the controller, a nominal command based, at least in part, on the estimated state; and predicting, via a nonlinear optimal control model programmed in the controller, an optimized command based, at least in part, on the estimated state and the nominal command, the optimized command configured to satisfy one or more operational constraints during operation of the power generating asset; and controlling, via the controller, the power generating asset based on the optimized command.

[0090] The power generating asset of any preceding clause, wherein the voltagedrop grid event comprises one of a low-voltage ride through event (LVRT) or a multifault ride through (MFRT) event.

[0091] The power generating asset of any preceding clause, wherein the optimized command specifies one of a generator torque, a current, and a blade pitch.

[0092] The power generating asset of any preceding clause, wherein predicting, via the nonlinear optimal control model programmed in the controller, the optimized command based, at least in part, on the estimated state and the nominal command further comprises: outputting, via the nonlinear optimal control model, a time series of values for the optimized command, the time series having a finite horizon, wherein the nonlinear optimal control model comprises one of nonlinear model predictive control, linear model predictive control, or parametric control; and selecting a first instance in the time series to be the optimized command.

[0093] The power generating asset of any preceding clause, wherein the power and load optimization function further comprises providing the time series as feedback to the nonlinear optimal control model.

[0094] The power generating asset of any preceding clause, wherein the power and load optimization function further comprises: determining, via the supervisory control model, an additional nominal command based, at least in part, on the estimated state; and predicting, via the nonlinear optimal control model, an additional701077-WO-1 / GECW-1287-PCToptimized command based, at least in part, on the estimated state and the additional nominal command, the optimized command being one of a torque, a current, or a blade pitch, and the additional optimized command being another of the torque, the current, or the blade pitch.

[0095] The power generating asset of any preceding clause, wherein the power and load optimization function further comprises: outputting, via the nonlinear optimal control model, a time series of a set of values for the optimized command and the additional optimized command, the time series having a finite horizon, wherein the nonlinear optimal control model includes nonlinear model predictive control; selecting a first set in the time series to be the optimized command and the additional optimized command; and providing the time series as feedback to the nonlinear optimal control model.

[0096] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they include structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.

Claims

701077-WO-1 / GECW-1287-PCTWHAT IS CLAIMED IS:

1. A method for controlling a power generating asset connected to an electrical gnd, the power generating asset having a power converter and a drivetrain with a generator, the method comprising:receiving, via a controller, an indication of a voltage-drop grid event occurring in the electrical grid;during recovery from the voltage-drop grid event, implementing, via the controller, a power and load optimization function, the power and load optimization function comprising:estimating a state of the power generating asset;determining, via a supervisory control model programmed in the controller, a nominal command based, at least in part, on the estimated state; andpredicting, via a nonlinear optimal control model programmed in the controller, an optimized command based, at least in part, on the estimated state and the nominal command, the optimized command configured to satisfy one or more operational constraints during operation of the power generating asset; andcontrolling, via the controller, the power generating asset based on the optimized command.

2. The method of claim 1, wherein the voltage-drop grid event comprises one of a low-voltage ride through event (LVRT) or a multi-fault ride through (MFRT) event.

3. The method of claim 1, wherein the optimized command comprises at least one of a generator torque command, a current command, or a blade pitch command.

4. The method of claim 1, wherein the optimized command is predicted based further on at least one of a reference operating parameter and a weight to the nonlinear optimal control model.701077-WO-1 / GECW-1287-PCT5. The method of claim 4. further comprising determining the reference operating parameter and the weight based on the estimated state and data associated with operation of the power generating asset.

6. The method of claim 1, wherein predicting, via the nonlinear optimal control model programmed in the controller, the optimized command based, at least in part, on the estimated state and the nominal command further comprises:outputting, via the nonlinear optimal control model, a time series of values for the optimized command, the time series having a finite horizon; andselecting a first value in the time series to be the optimized command.

7. The method of claim 6, wherein the power and load optimization function further comprises providing the time series as feedback to the nonlinear optimal control model.

8. The method of claim 1, wherein the power and load optimization function further comprises:determining, via the supervisory control model, an additional nominal command based, at least in part, on the estimated state; andpredicting, via the nonlinear optimal control model, an additional optimized command based, at least in part, on the estimated state and the additional nominal command.

9. The method of claim 8. wherein the optimized command comprises one of a torque command, a current command, or a blade pitch command, and the additional optimized command being another of the torque command, the current command, or the blade pitch command.

10. The method of claim 8, wherein the power and load optimization function further comprises:701077-WO-1 / GECW-1287-PCToutputting, via the nonlinear optimal control model, a time series of a set of values for the optimized command and the additional optimized command, the time series having a finite horizon, wherein the nonlinear optimal control model includes nonlinear model predictive control; andselecting a first set in the time series to be the optimized command and the additional optimized command.

11. The method of claim 10, wherein the power and load optimization function further comprises providing the time series as feedback to the nonlinear optimal control model.

12. The method of claim 1, wherein the nonlinear optimal control model is configured to predict the optimized command by minimizing a cost function while satisfying the one or more operational constraints.

13. The method of claim 1. wherein the one or more operational constraints comprise at least one of a component load constraint, a component torque constraint, and a correction in the component torque constraint.

14. A power generating asset connected to an electrical grid, the power generating asset comprising:a generator;a power converter coupled to the generator; anda controller comprising at least one processor configured to perform a plurality of operations, the plurality of operations comprising:receiving, via a controller, an indication of a voltage-drop grid event occurring in the electrical grid;during recovery from the voltage-drop grid event, implementing, via the controller, a power and load optimization function, the power and load optimization function comprising:estimating a state of the power generating asset;701077-WO-1 / GECW-1287-PCTdetermining, via a supervisory control model programmed in the controller, a nominal command based, at least in part, on the estimated state; andpredicting, via a nonlinear optimal control model programmed in the controller, an optimized command based, at least in part, on the estimated state and the nominal command, the optimized command configured to satisfy one or more operational constraints during operation of the power generating asset; andcontrolling, via the controller, the power generating asset based on the optimized command.

15. The power generating asset of claim 14, wherein the voltage-drop grid event comprises one of a low-voltage ride through event (LVRT) or a multi-fault ride through (MFRT) event.

16. The power generating asset of claim 14, wherein the optimized command specifies one of a generator torque, a current, and a blade pitch.

17. The power generating asset of claim 14, wherein predicting, via the nonlinear optimal control model programmed in the controller, the optimized command based, at least in part, on the estimated state and the nominal command further comprises:outputting, via the nonlinear optimal control model, a time series of values for the optimized command, the time series having a finite horizon, wherein the nonlinear optimal control model comprises one of nonlinear model predictive control, linear model predictive control, or parametric control; andselecting a first instance in the time series to be the optimized command.

18. The power generating asset of claim 17, wherein the power and load optimization function further comprises providing the time series as feedback to the nonlinear optimal control model.701077-WO-1 / GECW-1287-PCT19. The power generating asset of claim 13, wherein the power and load optimization function further comprises:determining, via the supervisory control model, an additional nominal command based, at least in part, on the estimated state; andpredicting, via the nonlinear optimal control model, an additional optimized command based, at least in part, on the estimated state and the additional nominal command, the optimized command being one of a torque, a current, or a blade pitch, and the additional optimized command being another of the torque, the current, or the blade pitch.

20. The power generating asset of claim 19, wherein the power and load optimization function further comprises:outputting, via the nonlinear optimal control model, a time series of a set of values for the optimized command and the additional optimized command, the time series having a finite horizon, wherein the nonlinear optimal control model includes nonlinear model predictive control;selecting a first set in the time series to be the optimized command and the additional optimized command; andproviding the time series as feedback to the nonlinear optimal control model.

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