Systems and methods for adaptive power system stabilizers (PSS)

Abstract

An electric power generation system including an adaptive power system stabilizer (PSS), the adaptive PSS including a first estimator configured to receive a plurality of sensor measurements as inputs and to output derived infinite bus (IB) values, the adaptive PSS further including a second estimator disposed downstream of the first estimator and configured to switch between a plurality of models, each of the plurality of models configured to receive the derived IB values ​​as inputs and to output derived electric generator parameters, the adaptive PSS configured to provide stabilization of the electric generator using the derived electric generator parameters.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to and the benefit of French Patent Application No. 2211107, filed October 26, 2022, entitled "SYSTEMS AND METHODS FOR AN ADAPTIVE POWER SYSTEM STABILIZER (PSS)," which is incorporated herein by reference in its entirety.

[0002] The subject matter disclosed herein relates to power system stabilizers, and more particularly to adaptive power system stabilizers.

[0003] Certain power production systems may include generators and distributed generators that may be powered by turbine systems, such as, but not limited to, gas turbine systems. The gas turbine systems may provide a motive force suitable for rotating generators to generate electrical power, for example. The turbine and generator systems may include one or more controllers suitable for providing various control functions, such as control of turbine speed, load, generator voltage, reactive power flow, and the overall stability of the power production system. During operation, the power production systems may be electrically coupled to a power grid, such as a city or municipal power grid. However, under certain operating conditions of the power grid, transient conditions may occur. It would be beneficial to improve the handling of transient conditions through a power system stabilizer (PSS). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] European Patent Application Publication No. 4007106 Summary of the Invention

[0005] Certain embodiments commensurate in scope with the originally claimed invention are summarized below. These embodiments are not intended to limit the scope of the claimed invention; rather, these embodiments are intended merely to provide a brief summary of possible forms of the invention. Indeed, the invention may encompass a variety of forms that may be similar to or different from the embodiments set forth below.

[0006] In a first embodiment, an electric power generation system includes an adaptive power system stabilizer (PSS). The adaptive PSS includes a first estimator configured to receive a plurality of sensor measurements as inputs and to output derived infinite bus (IB) values. The adaptive PSS further includes a second estimator disposed downstream from the first estimator and configured to switch between a plurality of models, each of the plurality of models configured to receive the derived IB values ​​as inputs and to output derived electric generator parameters, and the adaptive PSS configured to provide stabilization of the electric generator using the derived electric generator parameters.

[0007] In a second embodiment, a method includes obtaining a plurality of sensor measurements via a sensor network and deriving an infinite bus (IB) value via a first estimator, the first estimator configured to use the plurality of sensor measurements as inputs and output the IB value. The method further includes deriving derived electric generator parameters via a second estimator disposed downstream of the first estimator, the second estimator configured to switch between a plurality of models, each of the plurality of models configured to use the IB value as an input and output the derived electric generator parameter. The method also includes stabilizing the electric generator via an adaptive power system stabilizer (PSS) based on the derived electric generator parameters.

[0008] In a third embodiment, a non-transitory computer-readable medium storing computer-executable code is provided, the code including instructions for obtaining a plurality of sensor measurements via a sensor network and instructions for deriving an infinite bus (IB) value via a first estimator, the first estimator configured to use the plurality of sensor measurements as inputs and output the IB value. The code also includes instructions for deriving derived electric generator parameters via a second estimator disposed downstream of the first estimator, the second estimator configured to switch between a plurality of models, each of the plurality of models configured to use the IB value as an input and output the derived electric generator parameter. The code further includes instructions for stabilizing the electric generator via an adaptive power system stabilizer (PSS) based on the derived electric generator parameters.

[0009] These and other features, aspects, and advantages of the present invention will be better understood from the following detailed description taken in conjunction with the accompanying drawings, in which like reference numerals represent like parts throughout the drawings. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram of an embodiment of an electrical power production system having an adaptive power system stabilizer; [Figure 2] FIG. 2 is a block diagram illustrating an embodiment of a first estimator that may be coupled to a second estimator, where the first estimator and / or the second estimator may be included in the adaptive power system stabilizer of FIG. [Figure 3] FIG. 10 is a block diagram illustrating further details of an embodiment of a second estimator with a switchable model. [Figure 4] 2 is a flow chart illustrating one embodiment of a process suitable for applying the adaptive power system stabilizer of FIG. 1. DETAILED DESCRIPTION OF THE INVENTION

[0011] One or more specific embodiments of the present invention are described below. While an effort is made to provide a concise description of these embodiments, not all features of an actual implementation may be described herein. It should be understood that in the development of any such actual implementation, as with any engineering or design project, numerous implementation-specific decisions must be made to achieve the developer's particular goals, including compliance with system-related and business-related constraints that may vary from implementation to implementation. Moreover, it should be understood that such a development effort may be complex and time-consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those skilled in the art having the benefit of this disclosure.

[0012] When introducing elements of various embodiments of the invention, the articles "a," "an," "the," and "said" are intended to mean that there are one or more of the element. The terms "comprising," "including," and "having" are intended to be inclusive and mean that there may be additional elements other than the listed elements.

[0013] The present embodiments relate to systems and methods for power system stabilization of generators that may be connected to prime movers, such as, but not limited to, gas turbine systems, steam turbine systems, hydroelectric turbine systems, wind turbine systems, nuclear turbine systems, or any combination thereof. In particular, an adaptive power system stabilizer (PSS) system is provided for continuously and adaptively determining to apply PSS setpoints to dampen one or more of various oscillatory frequency ranges (e.g., inter-tie frequency ranges, local frequency ranges, intra-plant frequency ranges, etc.) based on cascaded models (e.g., cascaded models). For example, power generation by renewable energy sources (e.g., wind, solar, etc.) can cause grid frequency changes (e.g., transients). Thus, when the source of the transient causes a particular transient change (e.g., above or below a threshold), less synchronous inertia may be present, and an increased rate of change of frequency conditions may result in a reaction of the power production system. Similarly, the power grid can become more dynamic through the use of certain renewable energy technologies (e.g., solar power plants, wind power plants, hydroelectric power plants) whose operational power production can vary due to cloud patterns, wind conditions, rain, etc.

[0014] The techniques described herein involve the use of a cascaded set of estimators, where a first estimator in the cascaded set can derive an infinite bus value, such as a voltage value at the infinite bus, as well as an external reactance. Indeed, rather than treating the infinite bus as a constant, the first estimator can account for infinite bus variations, such as those caused by a renewable energy power production system. The derived infinite bus value can then be used as an input to a second estimator downstream of the first estimator. The second switchable estimator can model machine details (e.g., a generator) and use certain internal variables or parameters of the first estimator. The second estimator can include switching logic for switching between various models, as further described below. The output of the second estimator can then be used by the adaptive PSS to improve power generation, for example, by adjusting a specific signal sent to an automatic voltage regulator (AVR), which is useful for damping or eliminating system oscillations via the AVR. Thus, the techniques described herein can provide increased stability and improved power output even for renewable energy sources that are connected to the power grid.

[0015] As used herein, “power system stability” may refer to, at least, the ability of an electric power system and associated components (e.g., grid, generators, turbines, etc.) to transition, e.g., from a steady-state operating point (e.g., a nominal operating point) to, e.g., one or more other operating points (e.g., transient and / or dynamic operating points) after a perturbation, disturbance, or other undesirable impact on the electric power system. Additionally, as used herein, “damp,” “damping,” and / or “damped oscillations” may refer to the act or result of reducing the amplitude of oscillations over time. Similarly, “new operating parameters,” “new states,” or “new operating conditions” may refer to operating points and / or operating conditions to which an electric power system and associated components (e.g., grid, generators, turbines, etc.) may transition periodically and / or aperiodically during operation, e.g., after a perturbation, disturbance, or other undesirable impact on the electric power system.

[0016] With the above in mind, it may be useful to describe one embodiment of a power generation system, such as the exemplary power generation system 10 shown in FIG. 1 . The power generation system 10 may include various subsystems, such as a turbine 12, a generator 14, and an exciter 16. The turbine 12 (e.g., a gas turbine, a steam turbine, a hydroelectric turbine, etc.) may be coupled to the generator 14 via a shaft 13 and controlled by a turbine controller 15. The generator 14 may in turn be communicatively coupled to the generator's exciter 16. The exciter 16 may provide direct current (DC) to the generator's 14 field winding 22. In particular, the exciter 16 may provide a DC field current (e.g., a current utilized by the generator's 14 field winding 22 and / or other synchronous machines to establish a magnetic field for operation) to excite the generator's 14 magnetic field. For example, the exciter 16 may be a static (e.g., power electronic) exciter or a rotary (e.g., brush and / or brushless) exciter. In other embodiments, the exciter 16 may be bypassed and the power output may directly excite the field winding 22 of the generator 14. Also as shown, the output terminals of the generator 14 may be coupled to a large commercial power grid 26 via alternating current (AC) lines 28. Alternatively, the output terminals of the generator 14 may be coupled to a small industrial power generation plant.

[0017] The power generation system 10 may also include an excitation system 24 that may provide various control parameters to each of the generator 14 and / or exciter 16 based on, for example, measured parameters and / or indications of measured parameters received at one or more inputs to the excitation system 24. In certain embodiments, the excitation system 24 may function as an excitation control for the generator 14 and the exciter 16. The excitation system 24 may include one or more controllers 32 and one or more power converters 34. As generally shown, the controller(s) 32 may include one or more processors 36 and memory 38 that may collectively be used to support operating systems, software applications, systems, and the like useful for implementing the techniques described herein.

[0018] Power converter 34 may include a subsystem of integrated power electronic switching devices, such as silicon-controlled rectifiers (SCRs), thyristors, and insulated gate bipolar transistors (IGBTs), that receive alternating current (AC) power, DC power, or a combination thereof from a source, such as power grid 26. Excitation system 24 may receive this power via bus 29 and, based thereon, provide power, control, and monitoring to field winding 30 of exciter 16. Thus, excitation system 24 and exciter 16 may collectively operate to drive generator 14 according to a desired output (e.g., grid voltage, power factor, load frequency, torque, speed, acceleration, etc.). By way of example, in one embodiment, excitation system 24 may be an excitation control system, such as the EX2100e™ Excitation Control Regulator System available from General Electric Company, Schenectady, New York.

[0019] In certain embodiments, the power grid 26, and thus the turbine 12 and generator 14, may be susceptible to certain disturbances due to, for example, transient loss of power generation by the generator 14, switching of the power lines 28, load changes on the power grid 26, electrical faults on the power grid 26, etc. Such disturbances may cause the operating frequency of the turbine 12 and / or generator 14 (e.g., approximately 50 Hz in most European and Asian countries, approximately 60 Hz in North American countries) to experience undesirable oscillations that may result in transient and / or dynamic instability of the system 10. Such transient and / or dynamic instability may cause the generator 14, as well as the turbine 12 and exciter 16, to move from a steady-state operating point to a transient and / or dynamic operating point. Specifically, frequency deviations in the power grid 26 may cause rotor angle oscillations of the generator 14 (e.g., power angle oscillations) throughout the power system 10. Furthermore, conventional power system stabilizer (CPSS) systems (e.g., systems used to damp rotor angular oscillations of the generator 14) may generally be configured according to linear, fixed parameters, and therefore, unlike the adaptive PSS techniques described herein, CPSS systems are unable to effectively damp rotor angular oscillations of the generator 14 over the entire dynamic operating range of the generator 14 as desired.

[0020] As described in more detail below, in certain embodiments, the controller 32 of the excitation system 24 may include an adaptive power system stabilizer (PSS) system (shown in FIG. 2 ) that may be implemented as part of the excitation system 24 to, for example, dynamically and adaptively adjust (e.g., dynamically and adaptively damp) frequency oscillations of the rotor of the generator 14, thereby enhancing the ability of the system 10 to seamlessly move to transient and / or dynamic operating points, or substantially return to a steady-state operating point, or to withstand a transition to and maintain stable operation at a new steady-state operating point (e.g., derived by the adaptive PSS system). The adaptive PSS system may be coupled with an automatic voltage regulator (AVR), for example, and may use the AVR to damp certain oscillations, as described further below.

[0021] 2 is a block diagram of one embodiment of excitation system 24. More specifically, excitation system 24 is shown as including an adaptive power system stabilizer (PSS) system 50 and an automatic voltage regulator (AVR) 52. As previously mentioned, turbine 12, controlled by turbine controller 15, generates mechanical power (Pmec) that can be used to rotationally turn a rotor included in generator 14. As generally shown, controller(s) 15 may include one or more processors 17 and memory 19 that can collectively be used to support operating systems, software applications, systems, and the like useful for implementing the techniques described herein. Rotation of the rotor within a magnetic field can generate electrical power, which is then coupled to a transformer (X T ) 54 and line (X L ) 56. An infinite bus 58 is also shown.

[0022] An infinite bus 58 is conventionally described as a bus whose frequency and voltage remain constant regardless of the amount of load on the infinite bus. For example, a very large number of generators 14 (e.g., synchronous machines) can be connected to the bus, such that the bus is said to have infinite active and reactive power. Thus, electrical devices connected to the infinite bus typically do not affect other electrical devices when they are turned on or off. However, as more and more renewable energy sources (e.g., solar panels, wind turbines, hydroelectric turbines, etc.) are added to the infinite bus, large energy sources (e.g., 1 kW or more) can perturb other devices, such as generators 14, as they come online and offline. Therefore, the techniques described herein model the infinite bus, e.g., via an estimator, to behave as if actual voltage and / or frequency variations could exist.

[0023] In the illustrated embodiment, the first estimator 60 may include an infinite bus computation system 62 and a single state estimator 64. The estimator 64 may include, but is not limited to, a Kalman filter-type estimator. Inputs to the first estimator 60 may include measurements taken via a sensor network 66. The sensor network 66 may sense properties of the generator 14, such as voltage, amperage, active power, reactive power, slip, frequency, phase angle, noise, etc. The measurements from the sensor network 66 may be provided as inputs to the estimator 60 and a cascaded second estimator 68.

[0024] The first estimator 60 uses a single-state estimator 64 to estimate the external reactance X Emay be provided as an output (e.g., a state estimate). The infinite bus calculation system 62 may then provide derived infinite bus values ​​such as infinite bus voltage and / or frequency. In the illustrated embodiment, the single state estimator 64 may use certain model parameters such as a process noise covariance (qEK) that represents the uncertainty in the calculation of the process, and a sensor noise covariance (rEK) that represents the noise in the sensors (e.g., sensor network 66).

[0025] The single state estimator 64 calculates the generator stator current lst, the generator stator voltage Ust, and the phase angle φ, i.e., the angle between the stator voltage and the stator current, in accordance with the relationship lst 2 X E 2 +2·Ust·lst·sinφ·X E +Ust 2 -IB 2 = 0, where X E is the external reactance provided as output, and IB is X E is the infinite bus voltage for deriving I. Measurements of lst, Ust, and φ may be provided by a sensor network 66, and IB may be calculated by the system 62. A conventional Kalman-Busey filter, e.g., a linear quadratic estimator, or a sine wave estimator may be used to calculate X based on values ​​including historical values ​​for the remaining terms. E To solve the relationship lst 2 X E 2 +2·Ust·lst·sinφ·X E +Ust 2 -IB 2 Any type of single state estimator may be used, including but not limited to other types of estimators suitable for use with =0.

[0026] The second estimator 68 can use as inputs the mechanical power (Pmec), which may be provided by the turbine controller 15, the generator field voltage (Efd), which may be provided by the excitation system 24, and the IB voltage, which may be derived via the infinite bus calculation system 62. The second estimator 68 calculates the process noise covariance (Q EK ), sensor noise covariance (R EK ) can be used.

[0027] The second estimator 68 may estimate internal states of a particular generator 14 (e.g., a synchronous machine), such as the angle δ between the generator field voltage (EMF) and a reference voltage vector, the speed ω (e.g., RPM) of the generator 14, the internal voltage E′ of the generator 14, and the flux ψ in the generator 14. The second estimator 68 may estimate the external reactance X as described further below. E , and may additionally estimate some parameters of the machine model itself. Indeed, the second estimator 68 may model or otherwise include model parameters in addition to the machine parameters. The second estimator 68 may include, for example, switching logic 70 that may be used to switch between particular estimator models, as described further below.

[0028] The state of a particular generator 14, the external reactance X E Also shown is an extended model 72 that can provide as outputs the derivation of , and parameters of some generators 14. The extended model 72 can also output measurement estimates of power (active and reactive), current components, and / or voltage components. The extended model 72 may be a multi-state Kalman filter that embeds a model of generators 14 connected to an infinite bus 58 as follows:

[0029] Equation 1:X[k+1]=fState(X[k],u[k])+ω[k]

[0030] Formula 2: Z[k+1]=hMeasure(X[k+1],u[k])+v[k]

[0031] u=[Efd;Pmec;IB] T where ω is the process (model) noise and v is the measurement noise. X[k] represents the state of the system at time step k as a linear combination of the states at the previous time step X[k-1], and Z[k] represents the system measurements at time step k. The extended Kalman filter is described via Equation 1 and Equation 2, where u=[Efd;Pmec;IB] T It should be understood that other estimates may also be made that model a future state k, for example k+1, using the same.

[0032] In the illustrated embodiment, the extended model is adjustably coupled to a gain system 74. In use, the gain system 75 compares measurements coming from the sensor network 66 with measurements predicted by the extended model 72 (e.g., via a comparator 76) and can adjust the gain to minimize or eliminate the difference. The gain may be a constant (e.g., a positive or negative number), an equation, or a combination thereof. The first estimator 60 and / or the second estimator 68 may be included in the excitation system 24 or may be communicatively and / or operatively coupled to the excitation system 24. The first estimator 60 and / or the second estimator 68 may be provided as software, hardware, or a combination thereof. If implemented as software, the first estimator 60 and / or the second estimator 68 may be executable via the processor(s) 36 and stored in the memory 38.

[0033] the angle δ between the EMF and the reference voltage vector, the speed ω of the generator 14 (e.g., RPM), the internal voltage E′ of the generator 14, the magnetic flux ψk within the generator 14, and the external reactance X EThe output of the second estimator 68, such as a particular generator 14 (e.g., synchronous machine) internal state, such as generator 14 power (watts), generator 14 current, and / or generator 14 voltage, may then be used by the adaptive PSS 50, for example, to stabilize the generator 14. For example, the adaptive PSS 50 may inject voltage, current, etc. using the AVR 52 based on the output of the second estimator 68. In this manner, a more efficient and adaptive power generation system 10 may be provided.

[0034] 3 is a block diagram of one embodiment of the second estimator 68 including switchover logic 70 switchable via a switchover logic trigger 100. This figure includes some elements of FIG. 2, so like elements are numbered the same. In the illustrated embodiment, the second estimator 68 may include multiple switchable models, such as models 112, 114, and 116. When the power generation system 10 begins operation, the first model 112 may be used. The first model 112 may embed a model of the generator 14, as described above. For example, the first model 112 may include Equation 1 and Equation 2, described above, where u=[Efd;Pmec;IB] T where ω is the process (model) noise and v is the measurement noise. The second model 114 includes all of the first model 112, and may add one more state variable SV1. SV1 may be, for example, the external reactance X of the power network. E Similarly, the third model 116 may include all of the second model 114, including the state variable SV1, and add another state variable SV2, which may be a parameter of the extended model of the second estimator 68, for example, a variable such as synchronous reactance.

[0035] Indeed, one or more of the switchable models may include any of the variables of the extended model 72. The use of internal variables of the extended model 72 in one or more of the switchable models of the estimator 68 may result in adjustments of the extended model 72 in the estimator 68 to improve the predictive ability of the output of the estimator 68. It should be understood that more than three switchable models may be used. Indeed, four, five, six, seven, eight or more switchable models may be used to improve estimation. State variables (SVs) that may be used by the model include the external reactance X of the power network, E , any variables of the first estimator 60, any parameters of the extended model 72, etc.

[0036] The switching logic trigger 100 can switch from one model to the next (e.g., from the first model 112 to the second model 114, then from the second model 114 to the third model 116, and so on) by using time, by applying an error threshold, or a combination thereof. When using time, the switch from one model to the next can occur at regular intervals, such as 0.1 to 30 seconds, 0.5 to 5 hours, etc. When using an error threshold, the error can be calculated, for example, via the comparator 76 or using other comparisons. For example, the switching logic trigger 100 can calculate the external reactance X calculated by the first estimator 60. E and the external reactance X calculated by the second estimator 68. E and if the comparison is lower than a desired amount, a switch to the next model may be made. By applying switching logic to select a more complex model, the techniques described herein may enable a more accurate derivation by the second estimator 68.

[0037] 4 is a flowchart of one embodiment of a process 200 suitable for adjusting parameters of the adaptive PSS 50 via the cascaded estimators 60 and 68. The process 200 may be stored as computer instructions in the memory 38 and executed by the processor(s) 36. In the illustrated embodiment, the process 200 may obtain measurements (block 202) via the sensor network 66. For example, the sensor network 66 may include one or more sensors, such as voltage sensors, current sensors, inductance sensors, capacitance sensors, magnetic flux sensors, etc., located in the generator 14, in the transformer 54, on the line 56, and / or in the turbine system 12.

[0038] The process 200 may then derive a particular output (block 204) via the first estimator 60. As previously mentioned, the first estimator 60 may calculate the external reactance X E The first estimator 60 may include a single state estimator 64 that provides as an output (e.g., a state estimate) a voltage and / or frequency of the infinite bus 58. The first estimator 60 may also include an infinite bus calculation system 62 that may take the measurements collected in block 202 and derive the voltage and / or frequency of the infinite bus 58. The process 200 may then determine (block 206) which model to use using switching logic included in the second estimator 68. The model to use may be a multi-state model, such as an extended Kalman filter model.

[0039] The first model used during start-up of the power generation system 10 may be the first model 112. The first model 112 may be an extended Kalman filter that models the generator 14 via Equations 1 and 2 above. The process 200 may then derive (block 208) the output of the second estimator 68. The output of the second estimator 68 may include the angle δ between the EMF and the reference voltage vector, the speed ω (e.g., RPM) of the generator 14, the internal voltage E′ of the generator 14, the magnetic flux ψk within the generator 14, the external reactance X EThe input signal may include a particular generator 14 (e.g., synchronous machine) internal state, such as generator 14 power (watts), generator 14 current, and / or generator 14 voltage. The process 200 may then apply the output of the first estimator 60 and / or second estimator 68 for stabilization (block 210). For example, the adaptive PSS 50 may inject voltage, current, etc. using the AVR 52 based on the output of the first estimator 60 and / or second estimator 68. In this manner, a more efficient and adaptive power generation system 10 may be provided.

[0040] Technical effects of the disclosed embodiments include a power generation system having an adaptive power system stabilizer (PSS). The adaptive PSS may use a set of cascaded estimators, where the output of a first estimator is used as an input to a second estimator. In certain embodiments, the first estimator may include a single-state Kalman filter and an infinite bus computation system. The single-state estimator may be used to calculate an external reactance X E and the value of the infinite bus can be derived. E , the infinite bus values, and the variables of the first estimator may be used by the second estimator. The second estimator may include switching logic for switching between various models. Each subsequent model may include additional state variables in addition to the previous model. The switching logic may be time-based or error-threshold-based. By using a cascaded model system with switchable models, the techniques described herein can provide a more accurate and adaptive PSS that improves the stability of the power generation system.

[0041] The subject matter detailed above may be governed by one or more of the provisions set out below.

[0042] An electric power generation system including an adaptive power system stabilizer (PSS), the adaptive PSS including a first estimator configured to receive a plurality of sensor measurements as inputs and to output derived infinite bus (IB) values, the adaptive PSS further including a second estimator disposed downstream of the first estimator and configured to switch between a plurality of models, each of the plurality of models configured to receive the derived IB values ​​as inputs and to output derived electric generator parameters, the adaptive PSS configured to provide stabilization of the electric generator using the derived electric generator parameters.

[0043] 10. The system of claim 1, wherein the second estimator includes a first model included in the plurality of models, the first model configured to model one or more internal states of the electrical generator.

[0044] 10. The system of any one of the preceding clauses, wherein the one or more internal states include an angle δ between a generator field voltage (EMF) and a reference voltage vector, an electric generator speed ω, an electric generator internal voltage E, a magnetic flux in the electric generator, or a combination thereof.

[0045] 10. The system of any one of the preceding clauses, wherein the first model includes or is part of an extended Kalman filter.

[0046] The extended Kalman filter includes a multi-state Kalman filter that models X[k+1]=fState(X[k],u[k])+μ[k], Z[k+1]=hMeasure(X[k+1],u[k])+v[k], where u=[Efd;Pmec;IB] T 10. The system of any one of the preceding clauses, wherein Efd is the electric generator field voltage, Pmec is the mechanical power of a turbine mechanically coupled to the electric generator, Ib is a network voltage value, μ is model noise, and v is sensor noise of one or more sensors in the sensor network.

[0047] 10. The system of any one of the preceding clauses, wherein the second estimator includes a second model included in the plurality of models, the second model including the first model and an additional state variable.

[0048] 10. The system of any one of the preceding clauses, wherein the additional state variables include one or more internal variables.

[0049] 10. The system of any one of the preceding clauses, wherein the second estimator is configured to switch between the multiple models by waiting for a time to elapse and then switching, or by switching based on an error threshold, or a combination thereof.

[0050] 10. The system of any one of the preceding clauses, wherein the adaptive PSS is configured to provide stabilization of the electric generator using an automatic voltage regulator based on the derived electric generator parameters.

[0051] A method including: acquiring a plurality of sensor measurements via a sensor network; and deriving an infinite bus (IB) value via a first estimator, the first estimator configured to use the plurality of sensor measurements as inputs and output the IB value. The method further includes deriving derived electric generator parameters via a second estimator disposed downstream of the first estimator, the second estimator configured to switch between a plurality of models, each of the plurality of models configured to use the IB value as an input and output the derived electric generator parameter. The method also includes stabilizing the electric generator via an adaptive power system stabilizer (PSS) based on the derived electric generator parameters.

[0052] 10. The method of any one of the preceding clauses, wherein the second estimator includes a first model included in a plurality of models, the first model configured to model one or more internal states of the electrical generator.

[0053] 10. The method of any one of the preceding clauses, wherein the one or more internal states include an angle δ between a generator field voltage (EMF) and a reference voltage vector, an electric generator speed ω, an electric generator internal voltage E′, a magnetic flux in the electric generator, or a combination thereof.

[0054] The first model includes an extended Kalman filter that models X[k+1]=fState(X[k],u[k])+μ[k], Z[k+1]=hMeasure(X[k+1],u[k])+v[k], where u=[Efd;Pmec;IB] T wherein Efd is the electric generator field voltage, Pmec is the mechanical power of a turbine mechanically coupled to the electric generator, Ib is the network voltage value, μ is the model noise, and v is the sensor noise of one or more sensors in the sensor network.

[0055] 10. The method of any one of the preceding clauses, wherein the second estimator comprises a second model included in the plurality of models, the second model comprising the first model and an additional state variable.

[0056] 10. The method of any one of the preceding clauses, wherein the second estimator is configured to switch between the multiple models by waiting for a time to elapse and then switching, or by switching based on an error threshold, or a combination thereof.

[0057] A non-transitory computer-readable medium storing computer-executable code, the code including instructions for obtaining a plurality of sensor measurements via a sensor network and deriving an infinite bus (IB) value via a first estimator configured to use the plurality of sensor measurements as inputs and output the IB value. The code also includes instructions for deriving derived electric generator parameters via a second estimator disposed downstream of the first estimator, the second estimator configured to switch between a plurality of models, each of the plurality of models configured to use the IB value as an input and output the derived electric generator parameter. The code further includes instructions for stabilizing the electric generator via an adaptive power system stabilizer (PSS) based on the derived electric generator parameters.

[0058] 10. The non-transitory computer-readable medium of any one of the preceding clauses, wherein the second estimator includes a first model included in the plurality of models, the first model configured to model one or more internal states of the electrical generator.

[0059] 10. The non-transitory computer-readable medium of any one of the preceding clauses, wherein the one or more internal states include an angle δ between a generator field voltage (EMF) and a reference voltage vector, an electric generator speed ω, an electric generator internal voltage E′, a magnetic flux in the electric generator, or a combination thereof.

[0060] The first model includes an extended Kalman filter that models X[k+1]=fState(X[k],u[k])+μ[k], Z[k+1]=hMeasure(X[k+1],u[k])+v[k], where u=[Efd;Pmec;IB] Twherein Efd is the electric generator field voltage, Pmec is the mechanical power of a turbine mechanically coupled to the electric generator, Ib is a network voltage value, μ is model noise, and v is sensor noise of one or more sensors in the sensor network.

[0061] 10. The non-transitory computer-readable medium of any one of the preceding clauses, wherein the second estimator includes a second model included in the plurality of models, the second model including the first model and an additional state variable, and wherein the second estimator is configured to switch between the first model and the second model by waiting for a time to elapse, or by switching based on an error threshold, or a combination thereof.

[0062] This specification 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 methods incorporated therein. 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 have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements that have no substantial differences from the literal language of the claims.

[0063] The techniques presented and claimed herein are described and applied in terms of tangible objects and specific examples of practical nature that clearly improve the art, and are therefore not abstract, intangible, or mere theory. Furthermore, if any claim appended at the end of this specification contains one or more elements designated as "means for [performing] [a function]" or "steps for [performing] [a function]," such elements are intended to be construed under 35 U.S.C. §112(f). However, with respect to any claim containing elements designated in any other manner, such elements are not intended to be construed under 35 U.S.C. §112(f). [Explanation of symbols]

[0064] 10. Power Generation Systems 10 Power Systems 12 Turbine 12 Turbine System 13 Shaft 14 Generator 15 Turbine Controller 16 Exciter 17 processors 19 Memory 22 Field winding 24 Excitation System 26 Power Grid 28 Power Lines 29 busbar 30 field winding 32 Controller 34 Power Converter 36 processors 38 memory 50 Adaptive Power System Stabilizer (PSS) 52 Automatic Voltage Regulator (AVR) 54 Transformer 56 lines 58 Infinity bus 60 First Estimator 62 Infinite Bus Calculation System 64 Single State Estimator 66 Sensor Network 68 Second Estimator 70 Switching Logic 72 Expansion Model 74 Gain System 75 Gain System 76 Comparator 100 Switched logic trigger 112 First Model 114 Second Model 116 Third Model 200 processes Block 202 Block 204 Block 206 Block 208 210 blocks

Claims

1. An adaptive power system stabilizer (PSS) (50), comprising: a first estimator (60) configured to receive as input a plurality of sensor measurements and to output a derived infinite bus (IB) (58) value; a second estimator (68) disposed downstream of the first estimator (60) and configured to switch between a plurality of models, each of the plurality of models configured to receive as an input the derived IB (58) value and to output derived electric generator (14) parameters, and the adaptive PSS (50) configured to provide stabilization of the electric generator (14) using the derived electric generator (14) parameters; an adaptive power system stabilizer (PSS) (50) comprising: A power generation system (10) comprising:

2. 2. The power generation system of claim 1, wherein the second estimator includes a first model included in the plurality of models, the first model configured to model one or more internal states of the electric generator.

3. 3. The power generation system of claim 2, wherein the one or more internal states include an angle δ between a generator field voltage (EMF) and a reference voltage vector, an electric generator speed ω, an electric generator internal voltage E′, a magnetic flux within the electric generator, or a combination thereof.

4. The power generation system (10) of claim 2, wherein the first model (112) is part of an extended Kalman filter.

5. The extended Kalman filter includes a multi-state Kalman filter that models X[k+1] = fState(X[k], u[k]) + μ[k], Z[k+1] = hMeasure(X[k+1], u[k]) + v[k], where u = [Efd; Pmec; IB] Ｔ 5. The power generation system (10) of claim 4, wherein Efd is an electric generator (14) field voltage, Pmec is a mechanical power of a turbine (12) mechanically coupled to the electric generator (14), IB is a network voltage value, μ is a model noise, and v is a sensor noise of one or more sensors in the sensor network (66).

6. 3. The power generation system of claim 2, wherein the second estimator includes a second model included in the plurality of models, the second model including the first model and an additional state variable.

7. The power generation system (10) of claim 6, wherein the additional state variables include one or more internal variables.

8. 2. The power generation system of claim 1, wherein the second estimator is configured to switch between the multiple models by waiting for a time to elapse, or by switching based on an error threshold, or a combination thereof.

9. 9. The power generation system (10) of claim 8, wherein the adaptive PSS (50) is configured to provide stabilization of the electric generator (14) using an automatic voltage regulator (52) based on the derived electric generator (14) parameters.

10. acquiring a plurality of sensor measurements via a sensor network (66); deriving an infinite bus (IB) (58) value via a first estimator (60), the first estimator (60) configured to use the plurality of sensor measurements as inputs and output the IB (58) value; deriving derived electrical generator (14) parameters via a second estimator (68) disposed downstream of the first estimator (60), the second estimator (68) configured to switch between a plurality of models, each of the plurality of models configured to use the IB (58) value as an input and output the derived electrical generator (14) parameters; stabilizing the electric generator (14) via an adaptive power system stabilizer (PSS) (50) based on the derived electric generator (14) parameters; A method comprising:

11. 11. The method of claim 10, wherein the second estimator includes a first model included in the plurality of models, the first model configured to model one or more internal states of the electric generator.

12. 12. The method of claim 11, wherein the one or more internal states include an angle δ between a generator (14) field voltage (EMF) and a reference voltage vector, an electric generator speed ω, an electric generator (14) internal voltage E′, a magnetic flux within the electric generator (14), or a combination thereof.

13. The first model (112) includes an extended Kalman filter that models X[k+1] = fState(X[k], u[k]) + μ[k], Z[k+1] = hMeasure(X[k+1], u[k]) + v[k], where u = [Efd; Pmec; IB]. Ｔ 12. The method of claim 11, wherein Efd is an electric generator (14) field voltage, Pmec is the mechanical power of a turbine (12) mechanically coupled to the electric generator (14), IB is a network voltage value, μ is model noise, and v is sensor noise of one or more sensors in the sensor network (66).

14. 12. The method of claim 11, wherein the second estimator includes a second model included in the plurality of models, the second model including the first model and an additional state variable.

15. 11. The method of claim 10, wherein the second estimator (68) is configured to switch between the multiple models by waiting for a time to elapse, or by switching based on an error threshold, or a combination thereof.

16. A non-transitory computer readable medium storing computer executable code, the code comprising: instructions for obtaining a plurality of sensor measurements via a sensor network (66); instructions for deriving an infinite bus (IB) (58) value via a first estimator (60), the first estimator (60) configured to use the plurality of sensor measurements as inputs and output the IB (58) value; instructions for deriving derived electrical generator (14) parameters via a second estimator (68) disposed downstream of the first estimator (60), the second estimator (68) configured to switch between a plurality of models, each of the plurality of models configured to use the IB (58) value as an input and output the derived electrical generator (14) parameters; instructions for stabilizing the electric generator (14) via an adaptive power system stabilizer (PSS) (50) based on the derived electric generator (14) parameters; 1. A non-transitory computer-readable medium, comprising:

17. 17. The non-transitory computer-readable medium of claim 16, wherein the second estimator includes a first model included in the plurality of models, the first model configured to model one or more internal states of the electrical generator.

18. 20. The non-transitory computer-readable medium of claim 17, wherein the one or more internal states include an angle δ between a generator (14) field voltage (EMF) and a reference voltage vector, an electric generator speed ω, an electric generator (14) internal voltage E′, a magnetic flux within the electric generator (14), or a combination thereof.

19. The first model (112) includes an extended Kalman filter that models X[k+1] = fState(X[k], u[k]) + μ[k], Z[k+1] = hMeasure(X[k+1], u[k]) + v[k], where u = [Efd; Pmec; IB]. Ｔ 18. The non-transitory computer-readable medium of claim 17, wherein Efd is an electric generator (14) field voltage, Pmec is the mechanical power of a turbine (12) mechanically coupled to the electric generator (14), IB is a network voltage value, μ is model noise, and v is sensor noise of one or more sensors in the sensor network (66).

20. 20. The non-transitory computer-readable medium of claim 19, wherein the second estimator includes a second model included in the plurality of models, the second model including the first model and an additional state variable, and the second estimator is configured to switch between the first model and the second model by waiting for a time to elapse, by switching based on an error threshold, or by a combination thereof.

Images