Patent Document

FIELD OF THE INVENTION 
   The invention relates to combustion dynamics control, and more particularly, to systems and methods for using a combustion dynamics tuning algorithm with a multi-can combustor. 
   BACKGROUND OF THE INVENTION 
   Design and operation of a combustion system in a rotary machine such as a gas turbine engine can be complex. To operate such engines, conventional combustion dynamics tuning algorithms can utilize one or more sensors associated with various engine components to obtain performance and operating characteristics of the engine. For example, a single can combustor can utilize outputs from multiple combustion dynamic sensors to tune the combustor using a conventional dynamics tuning algorithm. In another example, a can annular-type combustor, which can include multiple cans arranged in an annular-shaped configuration, can utilize inputs from multiple combustion dynamic sensors, one for each can, to tune the combustor using another conventional dynamics tuning algorithm. To account for can-to-can variations, the latter type of dynamics tuning algorithm may check whether each of the sensors are within a predefined range, and then the sensors can be set to a median performance value, or alternatively, outputs from all of the sensors can be averaged to determine a dynamics signal to take action on. 
   In some instances, one or more sensors associated with a combustor, such as a single can combustor or can annular-type combustor, may provide poor or errant data or measurements. For example, a sensor may fail during combustor operation, and data from the sensor may cease or otherwise be considered errant or poor. If more than one sensor provides poor or errant data or measurements, such data or measurements may be input to the conventional dynamics tuning algorithm. In other instances, poor tuning or decreased efficiency can result in excessive vibration in or damage to the combustor. 
   Thus, there is a need for systems and methods for using a combustion dynamics tuning algorithm with a multi-can combustor. 
   BRIEF DESCRIPTION OF THE INVENTION 
   Embodiments of the invention can address some or all of the needs described above. Embodiments of the invention are directed generally to systems and methods for using a combustion dynamics tuning algorithm with a multi-can combustor. According to one embodiment of the invention, a method for controlling a gas turbine engine with an engine model can be implemented for an engine comprising multiple cans. The method can include obtaining operating frequency information associated with multiple cans of the engine. In addition, the method can include determining variation between operating frequency information of at least two cans. Furthermore, the method can include determining a median value based at least in part on the variation. Moreover, the method can include inputting the median value to an engine model, wherein based at least in part on the median value, the engine model determines an engine control action. In addition, the method can include outputting a control command to implement the engine control action. 
   According to another embodiment of the invention, a system for controlling a gas turbine engine with multiple cans can be implemented. The system can include a plurality of sensors adapted to obtain operating frequency information associated with a respective can. Furthermore, the system can include a model adapted to receive information from the plurality of sensors. The model can be adapted to determine a variation between operating frequency information of at least two cans. Furthermore, the model can be adapted to determine a median value based at least in part on the variation. In addition, the model can be adapted to determine an output based at least in part on the median value. Moreover, the system can include a controller adapted to determine an engine control action based at least in part on the output from the engine model, and further adapted to output a control command to implement the engine control action. 
   According to yet another embodiment of the invention, a system adapted to control a gas turbine engine with multiple cans, each can having at least one sensor can be implemented. The system can include at least one model adapted to estimate performance of a gas turbine engine. Furthermore, the system can include at least one estimator adapted to determine a current state of the engine, and initialize the model with operating frequency information from the sensors. In addition, the system can include at least one model-based control adapted to utilize an output from the estimator and to provide at least one control command to the gas turbine engine. 
   Other embodiments and aspects of embodiments of the invention will become apparent from the following description taken in conjunction with the accompanying drawings. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     Having thus described the invention in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein: 
       FIG. 1  is schematic diagram showing the layout of an example gas turbine engine that may be controlled by an embodiment of this invention. 
       FIG. 2  is a block diagram illustrating components of an engine control system according to an embodiment of the invention. 
       FIG. 3  is a block diagram illustrating an example combustion dynamics tuning model during execution according to one embodiment of the invention. 
       FIGS. 4-5  illustrate a series of example operating frequency data for a model and gas turbine engine according to embodiments of the invention. 
       FIGS. 6-7  illustrate a series of example correlations for operating frequency data for a model and gas turbine engine in accordance with embodiments of the invention. 
       FIGS. 8-9  illustrate a series of example proposed and estimated operating frequency data points for a model and gas turbine engine in accordance with embodiments of the invention. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   The invention now will be described more fully hereinafter with reference to the accompanying drawings, in which example embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the example embodiments set forth herein; rather, these embodiments are provided so that this disclosure will convey the scope of the invention to those skilled in the art. Like numbers refer to like elements throughout. 
   Embodiments of the invention are described below with reference to block diagrams and schematic illustrations of methods and systems according to embodiments of the invention. It will be understood that each block of the diagrams, and combinations of blocks in the diagrams can be implemented by computer program instructions. These computer program instructions may be loaded onto one or more general purpose computers, special purpose computers, or other programmable data processing apparatus to produce machines, such that the instructions which execute on the computers or other programmable data processing apparatus create means for implementing the functions specified in the block or blocks. Such computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in the block or blocks. 
   In embodiments of this invention, any physical system, control system or property of the engine or engine subsystem may be modeled, including, but not limited to, the engine itself, the gas path and gas path dynamics; actuators, effectors, or other controlling devices that modify or change any engine behavior; sensors, monitors, or sensing systems; the fuel metering system; the fuel delivery system; the lubrication system; and/or the hydraulic system. The models of these components and/or systems may be physics-based models (including their linear approximations). Additionally or alternatively, the models may be based on linear and/or nonlinear system identification, neural networks, and/or combinations of all of these. 
   Gas turbine engines are air breathing engines that produce work based on the Brayton thermodynamic cycle. Some non-limiting examples of gas turbine engines include: aircraft engines, power systems, propulsion engines for marine applications, turbines used as pumps, turbines used in combined cycle power plants, and turbines used for other industrial applications. In gas turbine engines, thermal energy is drawn from the combustion of fuel with air, the combustion of fuel with an oxidizer, chemical reactions and/or heat exchange with a thermal source. The thermal energy is then converted into useful work. This work can be output in the form of thrust, shaft power or electricity. The performance or operation of these engines is controlled through the use of actuators. Some non-limiting examples of actuators in gas turbine engines include fuel metering valves, inlet guide vanes, variable stator vanes, variable geometry, bleed valves, starter valves, clearance control valves, inlet bleed heat, variable exhaust nozzles, and the like. Some non-limiting examples of sensed engine values include temperatures, pressures, rotor speeds, actuator positions, and/or flows. 
   One example schematic of an example gas turbine engine  100  is shown in  FIG. 1 . The example engine  100  shown is a can annular combustor system such as the GE Energy Heavy Duty gas turbine series. Multiple cans  102 ,  104 ,  106 ,  108 ,  110 ,  112 ,  114 ,  116 ,  118 ,  120 ,  122 ,  124 , 126 ,  128 , also designated as numbers  1  through  14 , can be oriented in an annular-shaped configuration. Each can  102 - 128  can include at least one sensor, such as a dynamic pressure transducer, capable of measuring or otherwise detecting an operating frequency of the can or engine component. An example of a suitable sensor is disclosed in U.S. Pat. No. 6,708,568. Signals from each sensor can be processed using spectral analysis or similar techniques to isolate a frequency of interest. 
   In one embodiment, operating frequency data from each can  102 - 128 , such as dynamic pressure measurements, can be processed using a Fast Fourier transformation to determine the frequency content and amplitudes of the frequencies. Using this information, a frequency distribution such as a histogram can be generated. Based at least in part on the histogram, a representative operating frequency can be selected for the particular can or engine component. As shown in  FIG. 3 , operating frequency data or selected representative operating frequencies for each can  102 - 128  can be used as an input, such as  330 , to an example combustion dynamics tuning model and algorithm. It will be understood that “operating frequency information” and “operating frequency data” can be used interchangeably, and that both phrases can include, but are not limited to, operating data, operating pressures, dynamic operating pressures, and operating amplitude data. 
   It will be understood by those skilled in the art that the embodiments described herein may be applicable to a variety of systems and are not limited to engines or other devices similar to that described in  FIG. 1 . 
     FIG. 2  illustrates a control arrangement implementing an example engine model according to an embodiment of the invention. The control system  200  shown in  FIG. 2  is adapted to monitor and control the physical engine plant or gas turbine engine  210  to provide substantially optimal performance under a variety of conditions. The plant or engine  210  can include sensors which sense or measure values Y of certain parameters. These parameters can include, but are not limited to, fan speed, operating frequencies, dynamic pressures, operating pressures, operating pressure ratios, and temperatures. The plant or engine  210  can also include one or more actuators which can be controlled by one or more command inputs U. The plant or engine  210  may be similar to, for example, the engine  100  illustrated in  FIG. 1 . 
   The values Y of the sensed or measured parameters are provided to a state estimator  220 . The values input to the state estimator  220 , such as sensor inputs, operating frequencies or dynamic pressures, can be used to initialize one or more values in the state estimator  220 . The state estimator  220  can include an engine model or model  230  of the plant or engine  210 . The model  230  can be used by the state estimator  220  to generate one or more state parameters which can include estimates of performance parameters. One example of a suitable engine model is described in further detail as  300  in  FIG. 3 . 
   The state parameters from the state estimator  220  and associated model  230  can be transmitted to a model-based predictive control module or control module  240 . The control module  240  can use the state parameters to perform an optimization to determine commands for one or more actuators of the plant or engine  210 . For example, the control module  240  can perform an optimization to determine one or more engine control actions and corresponding control commands for one or more actuators of a gas turbine engine. In this regard, the control module  240  can include an optimizer  250  and a model  260 . The model  260  associated with the control module  240  may be identical to the model  230  associated with the state estimator  220 . Those skilled in the art will recognize that an engine model or model can be implemented in either or both the state estimator  220  and control module  240 . Using either or both of the models  230 ,  260  allows optimization of the engine  210  to converge rapidly. 
   In use, embodiments of the invention can be utilized to initialize an engine model, such as models  230 ,  260 , on startup of the plant or engine  210 . Furthermore, embodiments of the invention can be utilized to re-initialize the dynamic states of the model, such as models  230 ,  260 , after any time of event, such as load rejection or a sensor failure. Other embodiments of the invention can be used to initialize dynamic states of other types of machines or devices in other circumstances. 
     FIG. 3  is a schematic diagram illustrating an example engine model during initial configuration and also during normal execution according to embodiments of the invention. This diagram illustrates data processing by various modules associated with an engine model or model  300  such as a combustion dynamics tuning algorithm model. As shown, the model  300  can include some or all of the following modules in accordance with embodiments of the invention: sensor health block  302 ; median block  304 ; transfer function (TF) tuning block  306 ; a memory block  308 ; median dynamics block  310 ; model based control algorithm block  312 ; standard deviation block  314 ; mean block  316 ; covariance block  318 , constant block  320 ; median dynamics block  322 ; median target block  324 ; and a memory block  326 . The module blocks  302 - 326  represent various “run time”-type modules for which various parameters can be input to each of the modules  302 - 326 , and respective corresponding outputs can be received from the modules  302 - 326  in accordance with embodiments of the invention. Those skilled in the art will recognize that various inputs and outputs can be configured as data inputs, vectors, matrices, functions, and other mathematical-type devices. In any instance, the example model  300  shown can determine model predictions and dynamically tune combustion model predictions to measured performances in a real time environment for a gas turbine engine, such as  100  in  FIG. 1 , or a similar device. The example model  300  can be implemented with the gas turbine engine shown as  100  in  FIG. 1 , and the system shown as  200  in  FIG. 2 . 
   Sensor health block  302  receives one or more inputs  328  from an engine  330 , similar to engine  100  shown in  FIG. 1 . For example, the inputs can be operating frequency information or dynamic pressure information from one or more sensors associated with respective cans oriented in an annular-shaped configuration. In the embodiment shown in  FIG. 3 , inputs from 14 sensors, one for each can of can-annular type engine can be obtained. In addition, the sensor health block  302  can determine whether some or all of the inputs  328  are valid input signals. In one embodiment, the sensor health block  302  can determine whether some or all of the inputs are within a predefined range by comparing the inputs  328  to a previously stored set of data. 
   In other embodiments, any number of inputs from the engine, or any number of cans associated with the engine can be input to the sensor health block  302 . 
   In one embodiment, a determination whether to use some or all of the inputs  328  can be made depending on whether some or all of the inputs  328  are within a predefined range. Other embodiments may include different types of input signal validity checks, such as a simple range check or application of an algorithm to determine or evaluate input signal validity. In the event that some or all of the inputs  328  do not meet input signal validity checks or are not within a predefined range, some or all of the inputs  328  can be rejected, and no further action with respect to some or all of the inputs  328 . Alternatively, additional data may be used to replace some or all of the inputs  328 . In the event that some or all of the inputs  328  are found to be valid input signals or are within a predefined range, some or all of the inputs  328  can be further processed by other components of the engine model, such as model  300 . 
   In the event that some or all of the inputs are found to be valid input signals or are within a predefined range, some or all of the inputs can be transmitted via  332  to the median block  304 . The median block  304  can determine a median value  334  based on some or all of the inputs  330  transmitted. The median value  334  can be transmitted to the transfer function (TF) tuning block  306  for storage in and subsequent retrieval from memory block  308 . In addition, the median value  334  can be input to the median dynamics transfer function (TF) block  310 . In this manner, the transfer function (TF) tuning block  306  can utilize the median value  334  to tune, or modify, the median dynamics transfer function (TF) block  310  in order to reduce the difference between the median value  334  and the median dynamics transfer function (TF) block  310 . The memory block  308  may be used to store and process the tuning variable data used to tune or modify the median dynamics transfer function (TF) block  310 . 
   The median dynamics transfer function (TF) block  310  can receive input, or can otherwise be tuned or modified using the median value  334  with a median dynamics transfer function to determine an input “M hat”  336  to the model based control algorithm block  312 . As shown by the multiple input arrows to the median dynamics transfer function (TF) block  310 , additional median values for other operating frequencies can be input and simultaneously processed. In one embodiment, multiple inputs to the median dynamics transfer function (TF) block  310  can be implemented, and the output of block  310  may be a function of any number of different operating parameters and constants. 
   Utilizing the median value  334  associated with the input “M hat”  336  and the accompanying tuning variable from  306  and/or  308 , control of the engine  330  by the model based control algorithm block  312  may be prone to problems when variations between can-to-can operating frequencies of the engine  330  are relatively large. The median transfer functions can be functions of operating conditions including, but not limited to, fuel flow, combustor fuel splits, fuel temperature, fuel composition, combustor pressure, and combustor airflow. 
   Referring back to sensor health block  302 , some or all of the inputs  328 , such as operating frequency information, is input to standard deviation block  314  via  338 , where a standard deviation  340  can be determined. Furthermore, some or all of the inputs  328 , such as operating frequency information, is input to mean block  316  via  342 , where a mean  344  can be determined. Based at least in part on the standard deviation  340  and mean  344  input to the covariance block  318 , the covariance block  318  can determine covariance between the inputs  328  associated with the cans of the engine  330 . For example, the mean  344  can be divided by the standard deviation  340  to determine a covariance value  346  representative of the operation of the engine  330 . 
   In one embodiment, the covariance value  346  can be modified by an engine-dependent function, such as  348 . For example, an engine-dependent function can be determined based on prior data taken over time from one or more of a series of similar engines. Turning now to the constant block  320 , the covariance value  346  can be multiplied or otherwise adjusted by the engine-dependent function  348  to determine a “maximum to median” dynamics ratio  350  representative of the operation of the engine  330 . 
   Depending on prior operating performance of engine  330 , an upper specification limit (USL)  352  can be predefined based on the highest or maximum operating frequency or dynamic pressure that the engine  332  may be safely operated at, or any other desired upper operating limit. As represented by the median dynamics block  322 , the “maximum to median” dynamics ratio  350  can be adjusted or otherwise modified by the USL  352 . In this instance, maximum to median” dynamics ratio  350  can be divided by the USL  352  to obtain a median target  354 . 
   The median target  354  can be transmitted by the median target block  324  to be stored in memory block  326  for subsequent retrieval. Ultimately, the median target  354  can be input to the model based control algorithm block  312 . 
   Utilizing the median target  354 , control of the engine  330  by the model based control algorithm block  312  may be improved since variations between cans of the engine  330  can be accounted for. Control of the engine  330  in this manner can minimize the influence of poor sensor measurements by maintaining a maximum combustion dynamics limit on some or all of the cans associated with the engine  330 . In one embodiment, as the median target  354  is continuously calculated and input to the model based control algorithm block  312 , the control loop  302 - 310 ,  314 - 328 ,  332 - 354  is continuously “closed” and improved control of the engine  330  can result. In another embodiment, simultaneous or other real time processing of other operating frequencies can be performed and processed by the model  300  shown. 
   In use, some or all of the above processes and instructions can be used, and repeated as needed, to automatically and dynamically tune combustion in multiple cans of an engine, such as a can annular combustion engine, during model execution at any particular time. In this manner, the engine can be configured to “tune” the operating state of the combustion dynamics algorithm model to match measured dynamic performance of the engine or other device of interest. 
     FIGS. 4-9  illustrate various operating frequency data for a particular type of gas turbine engine implementing a combustion dynamics tuning model, similar to that described in  FIGS. 1-3 , in accordance with an embodiment of the invention.  FIGS. 4 ,  6 , and  8  illustrate operating frequency data, correlations, and proposed and estimated target values for one particular operating frequency; whereas  FIGS. 5 ,  7 , and  9  illustrate operating frequency data, correlations, and proposed and estimated target values for a different operating frequency. 
     FIGS. 4 and 5  each illustrate a series of example steady state-type operating frequency data  400 ,  500  for the gas turbine engine. In both  FIGS. 4 and 5 , approximately  50  data points are plotted along the x-axis  402 ,  502  and the peak-to-peak dynamic pressures (psi) of the data points are shown against the y-axis  404 ,  504 . In each Figure, maximum operating frequency data  406 ,  506  and median operating frequency data  408 ,  508  for each data point are shown. With reference to the data in these Figures, the maximum operating frequency data  506  for  FIG. 5  is relatively smooth in comparison to the maximum operating frequency data  406  for  FIG. 4 . In particular, the maximum operating frequency data  406  in  FIG. 4  appears to increase significantly between data points  24 - 30 , whereas the maximum operating frequency data  506  in  FIG. 5  remains relatively constant throughout the data points shown. 
   Generally, depending on the maximum operating frequency data, a median value, similar to the median value  334  described with respect to median block  304  in  FIG. 3  can be selected for the operating data at a particular peak frequency. For example, using the maximum operating frequency data  506  in  FIG. 5 , a median value such as the value “2” can be selected since the maximum operating data  506  appears to remain constant at approximately the value of 2 psi against the y-axis  504 . In contrast, the maximum operating frequency data  406  in  FIG. 4  would not be suitable for selecting a median value, such as the value “2”, since the data  406  is not relatively smooth for the data points shown and the significant increase shown by a portion of the data points could adversely affect any selected median value. 
     FIGS. 6 and 7  illustrate the implementation of a combustion dynamics tuning model using the operating frequency data  400 ,  500  shown in  FIGS. 4 and 5  for the same gas turbine engine. In  FIGS. 6 and 7 , example “maximum to median” correlations  600 ,  700  between the respective predicted maximum operating data and measured maximum operating data of  FIGS. 4 and 5  are shown. In the embodiments shown in  FIGS. 6 and 7 , the following equation associated with the combustion dynamics tuning model was implemented:
 Predicted Max=Median×COV×Function (or Constant) 
   For the data in both Figures, median values (Median), covariances (COV), and engine-dependent function (Function or Constant) were determined for each data point and the resulting predicted maximum operating data was determined. Determination of the median values, covariances, and engine-dependent functions are similar to the determinations and calculations described with respect to the median value  334 , covariance value  346 , and engine-dependent function  348  described in  FIG. 3 . The resulting data points of  FIGS. 6 and 7  were plotted against the respective x-axis  602 ,  702  indicative of the measured maximum pressure, and the y-axis  604 ,  704  indicative of the predicted maximum pressure. As shown in both Figures, the “maximum to median” correlations  600 ,  700  for each set of operating frequency data are relatively straight line correlations. Thus, based on these correlations, a new median target, similar to  354  shown with respect to median target block  324  in  FIG. 3  can be determined or otherwise selected for use with the combustion dynamics tuning model, similar to  300  in  FIG. 3 . 
   Turning to  FIGS. 8 and 9 , the proposed peak (PK) median target data  800 ,  900  are respectively shown. In the embodiments shown, the following equation was implemented using the other existing data to determine estimated peak (PK) maximum.
 
Estimated PK Max=PK max−[(PK max/PK median)×(PK median−PK median target)]
 
   As a result of implementation of this equation, the estimated peak (PK) maximum data  802 ,  902  were determined. As shown by the estimated peak maximum data  802 ,  902  for both operating frequencies, the implemented embodiment of the combustion dynamics tuning model can hold the upper specification limit (USL) closer to a value of approximately 2 psi for the particular operating frequency data of the gas turbine engine shown. 
   Many modifications and other embodiments of the inventions set forth herein will come to mind to one skilled in the art to which these inventions pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Thus, it will be appreciated by those of ordinary skill in the art that the invention may be embodied in many forms and should not be limited to the embodiments described above. Therefore, it is to be understood that the inventions are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

Technology Category: f