This disclosure generally relates to gas turbines, and more specifically relates to systems and methods for controlling gas turbines.
Generally, gas turbines can have control systems that monitor and control their operation. These control systems can be used to control the combustion system of gas turbines as well as other operational aspects of gas turbines. In some instances, a gas turbine may be operating for an extended period such that operational and/or component efficiencies begin to degrade that result in various deleterious effects on compressor operation and gas turbine engine performance.
Embodiments are disclosed for systems and methods for controlling gas turbines. In an embodiment, a method for controlling a gas turbine can be provided. The method can include receiving measurement data associated with the operation of a gas turbine that includes an inlet bleed heat system to maintain a temperature of an airstream entering an inlet of the gas turbine. The method can further include determining, based on the measurement data, that a current operating state of the gas turbine is associated with a predefined risk. The method can further include identifying a predefined operation model that includes model operating parameters for operation of the inlet bleed heat system that minimizes the predefined risk. In addition, the method can include determining, based on the measurement data, the current operating state of the gas turbine and the one or more model operating parameters, that the one or more model operating parameters should be adjusted. Furthermore, the method can include generating one or more control signals to adjust the operation of the inlet bleed heat system based on the adjusted model operating parameters.
In another embodiment, a system for controlling a gas turbine can be provided. The system can include a gas turbine having an inlet bleed heat system operable to maintain a temperature of an airstream entering a compressor section of the gas turbine. The system can also include one or more processors operable to receive measurement data associated with the operation of the gas turbine. The one or more processors can further be operable to determine that a current operating state of the gas turbine is associated with a predefined risk based on the measurement data. The one or more processors can further be operable to identify a predefined operation model that includes one or more model operating parameters for operation of the inlet bleed heat system that minimizes the predefined risk. The one or more processors can further be operable to determine, based on the measurement data, the current operating state of the gas turbine and the one or more model operating parameters, that one or more model operating parameters should be adjusted. The one or more processors can further be operable to generate one or more control signals to adjust the operation of the inlet bleed heat system based on the one or more adjusted model operating parameters.
In yet another embodiment, there is disclosed one or more computer-readable media storing computer-executable instructions that, when executed by a processor, make the processor operable to receive measurement data associated with the operation of a gas turbine having an inlet bleed heat system that maintains a temperature of an airstream entering an inlet of the gas turbine and determine, based on the measurement data that a current operating state of the gas turbine is associated with a predefined risk. The processor is further operable to identify a predefined operation model that includes one or more model operating parameters for operation of the inlet bleed heat system that minimizes the predefined risk. The processor is further operable to determine, based on the measurement data, the current operating state of the gas turbine and the one or more model operating parameters, that the one or more model operating parameters should be adjusted. The processor is further operable to generate one or more control signals to adjust the operation of the inlet bleed heat system based on the adjusted model operating parameters.
Other embodiments, systems, methods, features, and aspects of the disclosure will become apparent from the following description taken in conjunction with the following drawings.
These implementations will now be described more fully below with reference to the accompanying drawings, in which various implementations and/or aspects are shown. However, various aspects may be implemented in many different forms and should not be construed as limited to the implementations set forth herein. Like numbers refer to like elements throughout.
The present disclosure relates to systems and methods for controlling the operation of gas turbines. In particular, certain embodiments of the disclosure can facilitate using inlet bleed heat control in order to achieve one or more objectives, such as, for instance, effectively removing ice that may have formed on gas turbine engine compressor inlet guide vanes, turndown and operating limit line compressor protection, and the like. As a result, certain embodiments can have the technical effect of controlling the performance of gas turbines and/or turbine components, thereby resulting in increased power output.
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Additionally, the control system 200 may be used to schedule the operation of the inlet bleed heating system 160. To do so, the control system 200 may identify one or more predefined operation models that can include any number of model operating parameters for operating of the inlet bleed heat system 160 of the gas turbine 140 in order to minimize the predefined risk. Based on the measurement data, the current operating condition of the gas turbine 140 and/or the model operating parameters, the control system 200 can be operable to determine an adjustment of the model operating parameters and generate one or more control signals that adjust the operation of the inlet bleed heat system 160 based on the adjusted modeled operating parameters, which significantly impact the output and efficiency of the gas turbine.
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Each control processor 202 may include one or more cores and can be configured to access and execute at least in part computer-executable instructions stored in the one or more memories 204. The one or more memories 204 can include one or more computer-readable storage media (“CRSM”). The one or more memories 204 may include, but are not limited to, random access memory (“RAM”), flash RAM, magnetic media, optical media, and so forth. The one or more memories 204 may be volatile in that information is retained while providing power or non-volatile in that information is retained without providing power.
The one or more I/O interfaces 206 may also be provided in each processor 202. These I/O interfaces 206 can allow for coupling a variety of input and/or output devices such as sensors, keyboards, mice, monitors, printers, external memories, and the like.
The one or more network interfaces 208 may provide for the transfer of data between the one or more processors 202 and another device directly such as in a peer-to-peer fashion, via a network, or both. The network interfaces 208 may include, but are not limited to, personal area networks (“PANs”), wired local area networks (“LANs”), wide area networks (“WANs”), wireless local area networks (“WLANs”), wireless wide area networks (“WWANs”), and so forth. The network interfaces 208 may utilize acoustic, radio frequency, optical, or other signals to exchange data between the one or more processors 202 and another device such as a smart phone, a tablet computer, a wearable computer, an access point, a host computer, and the like.
The one or more memories 204 may store computer-executable instructions or modules for execution by the one or more processors 202 to perform certain actions or functions. The following modules are included by way of illustration, and not as a limitation. Furthermore, while the modules are depicted as stored in the one or more memories 204, in some implementations, these modules may be stored at least in part in external memory which is accessible to the one or more processors 202 via the network interfaces 208 or the I/O interfaces 206. These modules may include an operating system (OS) module 210 configured to manage hardware resources such as the I/O interfaces 206 and provide various services to applications or modules executing on the one or more processors 202.
An operating state module 220 may be stored in at least one memory 204. The operating state module 220 may be configured to continuously and/or periodically acquire measurement data associated with the operation of a gas turbine. In certain embodiments, the measurement data may be received from, for instance, one or more sensor devices monitoring the operation of a gas turbine. In one embodiment, the operating state module 220 can be configured to determine, based in part on the measurement data, whether the inlet bleed heat system is turned on or off and/or potentially other data, if the current operating state of a gas turbine is associated with one or more predefined risks, such as, for instance, a risk of ice formation accumulating on the inlet of the gas turbine, a risk associated with turndown conditions of the gas turbine, and the like. The operating state module 220 may store the measurement data in one or more data files 245.
A predefined models module 230 may be configured to identify a predefined operation turbine model that can include one or more model operating parameters for operation of the inlet bleed heat system that minimizes the one or more predefined risks. Similar to the measurement data, the one or more model operating parameters may include a modeled ambient temperature, a modeled inlet temperature, a modeled angle of the inlet guide vanes, a modeled dew point temperature, and other data. The predefined models module 230 may store the model operating parameters in one or more data files 245.
Lastly, the control module 240 may be configured to determine if the one or more model operating parameters should be adjusted based in part on the measurement data, the current operating condition of the gas turbine and the model operating parameters. In certain embodiments, the control module 240 may then generate one or more control signals to adjust the operation of the inlet bleed heat system 160 of the gas turbine 140 based on the adjusted model operating parameters.
The one or more processors 202 described above with reference to
In block 305, a processor may receive measurement data associated with the operation of a gas turbine. For example, in at least one embodiment, a processor, such as processor 202 in
In block 310, a processor may analyze the measurement data received in block 305 and determine that the current operating state of the gas turbine is associated a predefined risk, such as, for example, the risk of ice formation accumulation on the inlet of the gas turbine, the risk of compressor failure, and the like. For instance, in at least one embodiment, a processor, such as processor 202 in
In block 315, a processor, such as processor 202 in
In block 320, a processor may adjust the one or more model operating parameters associated with a predefined operation model, such as, the predefined operating model identified in block 315, based at least in part on measurement data, such as the measurement data received in block 305, a current operating state of the gas turbine as determined in block 310, the one or more model operating parameters, and potentially other data. For example, a processor, such as processor 202 in
In block 325, a processor may generate one or more control signals to adjust the operation of an inlet bleed heat system based at least in part on the adjusted model operating parameters as determined in block 320. In at least one embodiment, a processor, such as processor 202 in
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The operations and processes described and shown above may be carried out or performed in any suitable order as desired in various implementations. Additionally, in certain implementations, at least a portion of the operations may be carried out in parallel. Furthermore, in certain implementations, less than or more than the operations described may be performed.
Certain aspects of the disclosure are described above with reference to block and flow diagrams of systems, methods, apparatuses, and/or computer program products according to various implementations. It will be understood that one or more blocks of the block diagrams and flow diagrams, and combinations of blocks in the block diagrams and the flow diagrams, respectively, can be implemented by processor-executable program instructions. Likewise, some blocks of the block diagrams and flow diagrams may not necessarily need to be performed in the order presented, or may not necessarily need to be performed at all, according to some implementations.
These processor-executable program instructions may be loaded onto a special-purpose computer or other particular machine, a processor, or other programmable data processing apparatus to produce a particular machine, such that the instructions that execute on the computer, processor, or other programmable data processing apparatus create means for implementing one or more functions specified in the flow diagram block or blocks. These program instructions may also be stored in a computer-readable storage media or 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 storage media produce an article of manufacture including instruction means that implement one or more functions specified in the flow diagram block or blocks. As an example, certain implementations may provide for a computer program product, comprising a computer-readable storage medium having a computer-readable program code or program instructions implemented therein, said computer-readable program code adapted to be executed to implement one or more functions specified in the flow diagram block or blocks. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational elements or steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions that execute on the computer or other programmable apparatus provide elements or steps for implementing the functions specified in the flow diagram block or blocks.
Accordingly, blocks of the block diagrams and flow diagrams support combinations of means for performing the specified functions, combinations of elements or steps for performing the specified functions and program instruction means for performing the specified functions. It will also be understood that each block of the block diagrams and flow diagrams, and combinations of blocks in the block diagrams and flow diagrams, can be implemented by special-purpose, hardware-based computer systems that perform the specified functions, elements or steps, or combinations of special-purpose hardware and computer instructions.
Many modifications and other implementations of the disclosure set forth herein will be apparent having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the disclosure is not to be limited to the specific implementations disclosed and that modifications and other implementations 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.