System and method for timely addressing particulates in a gas turbine with a cleaning system

Abstract

A method for timely addressing particulates in a gas turbine is provided. The gas turbine includes a compressor section, a combustion section, and a turbine section. The method includes monitoring, with a controller, data indicative of one or more particulate parameters using a particulate sensor. The particulate sensor is located at at least one of an inlet to the compressor section or an outlet of the turbine section. The method further includes determining, with the controller, when the data indicative of the one or more particulate parameters exceed a particulate threshold. The method further includes implementing a control action associated with a washing system in response to determining that the data indicative of the one or more particulate parameters exceed a particulate threshold.

Description

[Technical Field]

[0001] The present disclosure relates generally to systems and methods for timely addressing particulates in gas turbines with a washing system, and more particularly, to executing washing-related events based on particulate data sensed in the gas turbine. [Background technology]

[0002] Turbomachines are utilized in various industries and applications for the purpose of energy transfer. For example, a gas turbine engine generally includes a compressor section, a combustion section, a turbine section, and an exhaust section. The compressor section gradually increases the pressure of a working fluid entering the gas turbine engine and supplies the compressed working fluid to the combustion section. The compressed working fluid and fuel (e.g., natural gas) are mixed in the combustion section and combusted in a combustion chamber to produce high-pressure, high-temperature combustion gases. From the combustion section, the combustion gases flow into the turbine section, where they expand to produce work. For example, the expansion of the combustion gases in the turbine section can rotate a rotor shaft connected to, for example, a generator, to generate electricity. The combustion gases then exit the gas turbine through the exhaust section.

[0003] Gas turbines are used in many diverse applications and environments throughout the world. This diversity poses challenges to air filtration systems, requiring different particle accumulation estimates and / or estimates of impacts on gas turbine system components for each type of environmental contaminant(s), gas turbine platform technology, and / or fuel quality. For example, gas turbines operating in high-temperature, harsh climates, and / or high-efficiency gas turbines operating at high operating temperatures in environments where the turbine is subject to severe air quality pollution face significant challenges with respect to engine performance, reliability, and / or maintainability, especially when the gas turbine system's inlet system is compromised or damaged. Such challenges may include erosion, corrosion, and / or failure of various turbine components.

[0004] Over time, gas turbine components can deteriorate from use, material buildup, etc. For example, filters in a turbine system's filter house can deteriorate from the accumulation of sand, dust, or other particles, thereby causing an undesirable pressure drop in the inlet ductwork. In another example, a turbine system's compressor can also deteriorate from dust buildup, affecting the turbine system's power output. Furthermore, if the combustion equipment burns ash-forming fuels, ash or soot particles are transported by the combustion gases along the hot gas path and partially deposit in the hot parts, causing progressive fouling.

[0005] To clean various components of a gas turbine, component and subsystem washing events, or "water washes," can be statically scheduled. However, following a static schedule can lead to inefficient resource use by shutting down the gas turbine before the components actually reach a degraded state that affects the performance of the gas turbine. Furthermore, static scheduling can vary significantly depending on the operating environment of the gas turbine; for example, a gas turbine in a desert will require more frequent cleaning due to increased amounts of sand and other particles.

[0006] Thus, improved systems and methods for reducing gas turbine downtime by actively responding to particulate data in a washing system are desirable and would be understood in the art. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] U.S. Patent Application Publication No. 20180073386 Summary of the Invention

[0008] Aspects and advantages of methods and systems according to the present disclosure will be set forth in part in the description that follows, or will be obvious from the description, or may be learned by practice of the present technology.

[0009] According to one embodiment, a method for timely addressing particulates in a gas turbine is provided. The gas turbine includes a compressor section, a combustion section, and a turbine section. The method includes monitoring, with a controller, data indicative of one or more particulate parameters using a particulate sensor. The particulate sensor is located at at least one of an inlet to the compressor section or an outlet of the turbine section. The method further includes determining, with the controller, when the data indicative of the one or more particulate parameters exceed a particulate threshold. The method further includes, in response to determining that the data indicative of the one or more particulate parameters exceed a particulate threshold, implementing a control action associated with a washing system.

[0010] According to another embodiment, a system is provided. The system includes a gas turbine having a compressor section, a combustion section, and a turbine section. The system includes a washing system fluidly coupled to the gas turbine. The system further includes a particulate sensor disposed at at least one of an inlet to the compressor section or an outlet of the turbine section. The particulate sensor is configured to provide data indicative of one or more particulate parameters. The system further includes a controller communicatively coupled to the washing system and the particulate sensor. The controller includes a memory and at least one processor. The at least one processor is configured to perform a plurality of operations. The plurality of operations includes, with the controller, monitoring data indicative of the one or more particulate parameters with the particulate sensor. The plurality of operations further includes, with the controller, determining when the data indicative of the one or more particulate parameters exceed a particulate threshold. The plurality of operations further includes, in response to determining that the data indicative of the one or more particulate parameters exceed a particulate threshold, implementing a control action associated with the washing system.

[0011] These and other features, aspects, and advantages of the methods and systems of the present invention will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present technology and, together with the description, serve to explain the principles of the technology.

[0012] A full and enabling disclosure of the methods and systems of this invention, including the best mode of making and using the same, directed to one of ordinary skill in the art, is set forth in this specification, which makes reference to the accompanying figures. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a schematic diagram of a system including a turbomachine according to an embodiment of the present disclosure. [Figure 2] 1 illustrates a cross-sectional view of an inlet to a compressor section of a provided gas turbine in accordance with an embodiment of the present disclosure. [Figure 3] 2 shows a block diagram of the system shown in FIG. 1 according to an embodiment of the present disclosure. [Figure 4] 1 illustrates a framework for a real-time degradation / soiling advisory algorithm according to an exemplary embodiment of the present disclosure. [Figure 5] 1 illustrates a flowchart of steps for monitoring and responding to particulate influx in a gas turbine according to an exemplary embodiment of the present disclosure. [Figure 6] 1 illustrates a block diagram of one or more aspects of a model for monitoring and responding to particulate influx in a gas turbine, according to an embodiment of the present disclosure. [Figure 7] 1 illustrates a logic flow diagram in accordance with one or more exemplary aspects of the present disclosure. [Figure 8] 1 illustrates a flow diagram of a method for timely addressing particulates in a gas turbine according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0014] Reference will now be made in detail to embodiments of the method and system of the present invention, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the present technology, not as a limitation thereof. Indeed, it will be apparent to those skilled in the art that modifications and variations can be made in the present technology without departing from the scope or spirit of the claimed technology. For example, features illustrated or described as part of one embodiment can be used in another embodiment to yield still a further embodiment. Accordingly, the present disclosure is intended to cover such modifications and variations as come within the scope of the appended claims and their equivalents.

[0015] The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Moreover, unless otherwise specified, all embodiments described herein are to be considered exemplary.

[0016] The detailed description uses numerical and letter designations to refer to features in the drawings. Like or similar designations in the drawings and description are used to refer to like or similar parts of the invention. As used herein, the terms "first," "second," and "third" may be used interchangeably to distinguish one component from another and are not intended to denote the location or importance of the individual components.

[0017] The term "fluid" can be a gas or a liquid. The term "fluid communication" means that a fluid is capable of making a connection between designated areas.

[0018] As used herein, the terms "upstream" (or "forward") and "downstream" (or "aft") refer to relative directions with respect to fluid flow in a fluid pathway. For example, "upstream" refers to the direction from which fluid flows, and "downstream" refers to the direction from which fluid flows. However, as used herein, the terms "upstream" and "downstream" can also refer to electrical flow. The term "radially" refers to relative directions that are substantially perpendicular to the axial centerline of a particular component, the term "axially" refers to relative directions that are substantially parallel to and / or coaxially aligned with the axial centerline of a particular component, and the term "circumferentially" refers to relative directions that extend around the axial centerline of a particular component.

[0019] Approximate terms such as "approximately," "about," "generally," and "substantially" are not intended to be limited to the exact value stated. In at least some cases, approximating language can correspond to the precision of an instrument that measures a value, or the precision of a method or machine that constructs or manufactures a component and / or system. In at least some cases, approximating language can correspond to the precision of an instrument that measures a value, or the precision of a method or machine that constructs or manufactures a component and / or system. For example, approximating language can refer to within a margin of 1, 2, 4, 5, 10, 15, or 20% of a particular value, a range of values, and / or any of the endpoints defining the range of values. When used in the context of angles or directions, such terms include a range of plus or minus 10 degrees of the stated angle or direction. For example, "approximately vertical" includes directions within 10 degrees of any direction, e.g., clockwise or counterclockwise, from vertical.

[0020] Terms such as "coupled," "fixed," and "attached," unless expressly stated otherwise herein, refer to both direct coupling, fixing, or attachment, and indirect coupling, fixing, or attachment via one or more intermediate components or features. As used herein, the terms "comprises," "comprising," "includes," "including," "has," and "having," or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus comprising a list of features is not necessarily limited to only those features and may include other features not expressly listed or inherent to such process, method, article, or apparatus. Furthermore, unless expressly stated to the contrary, "or" refers to an inclusive or, not an exclusive or. For example, condition A or B can be satisfied by any one of the following: A is true (or present) and B is false (or absent), A is false (or absent) and B is true (or present), and both A and B are true (or present).

[0021] It should also be understood that, as used herein, the term "monitor" and variations thereof indicate that various sensors of the system may be configured to provide direct measurements of the monitored parameters and / or indirect measurements of such parameters.

[0022] Here, and throughout the specification and claims, range limitations are combinable and interchangeable, and unless the context and language dictate otherwise, such ranges are identified and include all subranges subsumed therein. For example, all ranges disclosed herein are inclusive of the endpoints, and the endpoints are independently combinable with each other.

[0023] 1 illustrates a schematic diagram of one embodiment of a system 100 including a gas turbine 10. While an industrial or land-based gas turbine is shown and described herein, the present disclosure is not limited to land-based and / or industrial gas turbines unless otherwise stated in the claims. For example, the invention described herein may be used with any type of turbomachine, including, but not limited to, a steam turbine, an aircraft gas turbine, or a marine gas turbine.

[0024] As shown, the gas turbine 10 generally includes an inlet section 12, a compressor section 14 disposed downstream from the inlet section 12, a plurality of combustors (not shown) in a combustion section 16 disposed downstream from the compressor section 14, a turbine section 18 disposed downstream from the combustion section 16, and an exhaust section 20 disposed downstream from the turbine section 18. Additionally, the gas turbine 10 may include one or more shafts 22 coupled between the compressor section 14 and the turbine section 18.

[0025] During operation, a working fluid, such as air, enters the compressor section 14 through the inlet section 12, where it is progressively compressed, providing compressed air to the combustors in the combustion section 16. The compressed air is mixed with fuel and burned in the respective combustors to generate combustion gases. The combustion gases flow through a hot gas path from the combustion section 16 to the turbine section 18, where energy (kinetic and / or thermal) is transferred from the combustion gases to rotor blades, rotating a shaft 22. This mechanical, rotational energy can then be used to power the compressor section 14 and / or generate electricity. The combustion gases exiting the turbine section 18 may then be exhausted from the gas turbine 10 via an exhaust section 20.

[0026] In many embodiments, the gas turbine 10 may include a series of inlet guide vanes (IGVs) 36 disposed at the inlet of the compressor section 14. The IGVs 36 may control the amount of air delivered to the compressor section 14. The IGVs 36 may be positioned at an angle that can be increased or decreased to allow less or more air into the compressor section.

[0027] In the exemplary embodiment, the system 100 may further include a wash system 102 fluidly coupled to the gas turbine 10. For example, the wash system 102 may include multiple wash lines 104 each extending between the wash system 102 and a nozzle 106 disposed on the gas turbine 10. Each of the wash lines 104 may supply a washing liquid (e.g., water mixed with one or more cleaning agents or detergents) to a respective nozzle 106 for washing various gas turbine 10 components. For example, a first wash line 104 may extend between the wash system and a first nozzle 106 coupled to the compressor section 14 to selectively wash components in the compressor section. A second wash line 104 may extend between the wash system 102 and a third nozzle 106 coupled to the turbine section 18 to selectively wash components in the turbine section. Each of the wash lines 104 may be in fluid communication with a wash liquid supply tank. In some implementations, during an offline water wash, the wash flow path (i.e., the flow path of the washing liquid) may include the combustion section 16.

[0028] In many embodiments, as shown, the washing system 102 can include a detergent selection system 108 and a detergent mixing system 110. The detergent selection system 108 can be operable to select a detergent or cleaning agent to mix (e.g., in the mixing system 110) with water or other fluids to form the washing liquid used during the washing process.

[0029] Components of the gas turbine 10 can be washed either online or offline (e.g., online washing or offline washing) using the washing system 102. In online washing, water (or water mixed with a detergent or cleaning agent) can be injected into the gas turbine 10 while the gas turbine 10 is running or operating. Online washing occurs while the gas turbine 10 maintains some form of power output, but may be below baseload (e.g., about 5% to about 20% below baseload, or, for example, about 10% to about 15% below baseload). Online washing can occur hourly, daily, monthly, quarterly, or any other recurring timeframe. Additionally, as described in more detail below, online washing may occur based on one or more sensed particulate parameters. For example, online washing can occur in response to determining, based at least in part, that one of a particulate inflow level, fouling rate, deposition rate, and degradation rate associated with one or more components of the gas turbine exceeds an online washing threshold, thereby prompting the washing system 102 to initiate online washing.

[0030] Offline washing can include shutting down and subsequent cooling of the gas turbine 10. Once the gas turbine 10 (particularly the turbine section 18) has cooled, water (or water mixed with a detergent or cleaning agent) can be injected into the gas turbine 10, for example, via the washing system 102. Shutting down the gas turbine 10 can allow for more thorough cleaning of the components, but the shutdown time of the gas turbine 10 can exceed the shutdown time resulting from an online wash. For example, the turbine system 10 can remain shut down (or non-operating) for 8 to 24 hours, depending on the completeness of the wash, the number of components undergoing washing, and the specific portion of the gas turbine 10 being washed. Thus, offline washing can occur less frequently than online washing (e.g., quarterly, annually, biennially, or any other recurring time frame, depending on the deterioration rate of the gas turbine 10). Additionally, as described in more detail below, offline washing may be performed in response to determining, based at least in part on, that one of the particulate inflow levels, fouling rates, deposition rates, and deterioration rates associated with one or more components of the gas turbine exceeds an offline washing threshold, thereby shutting down the gas turbine 10 and prompting the washing system 102 to initiate offline washing once the gas turbine 10 cools.

[0031] When particulates enter a gas turbine compressor, fouling occurs, changing the aerodynamic profile of the rotor blades and / or stator vanes through impact and frictional contact, ultimately reducing the overall efficiency of the compressor. The level of particulate inflow into a gas turbine can increase the fouling rate of compressor components. As described below, a controller can determine the compressor fouling rate based on the sensory data provided to one or more models and can take one or more control actions based on the magnitude of the fouling rate. The degradation rate, i.e., performance degradation, is determined by a decrease in the power output of the gas turbine over time as a result of the particulate inflow. For example, the more particulate inflow a gas turbine experiences, the more particulates accumulate in the compressor / turbine sections and / or cause fouling, thereby reducing the efficiency of the gas turbine system over time. As described below, a controller can determine the degradation rate based on the sensory data provided to one or more models and can take one or more control actions based on the magnitude of the degradation rate. Particulate buildup occurs when particulates enter the compressor / turbine and adhere to or bind to one or more compressor / turbine components, thereby affecting the aerodynamic profile of the components and reducing engine efficiency. As described below, the controller can determine the rate of particulate buildup in the compressor and / or turbine based on sensory data fed to one or more models, and can take one or more control actions based on the magnitude of the buildup rate.

[0032] The inlet section 12 of the gas turbine 10 may include a filter chamber 120 that may include one or more filters that prevent large particles (e.g., large particles such as sand, dust, dirt, etc.) from entering the compressor section 14. The filter chamber 120 may include a plurality of vane filters 122 that may filter large particles from the intake air 105, and an array of fabric filters 124 positioned downstream of the vane filters 122. The array of fabric filters 124 may be formed as any suitable filtering component and / or device that may be configured to filter particles, e.g., finer / smaller particulates, from the intake air flowing through the filter chamber 120.

[0033] The filter chamber 120 shown in FIG. 1 may include components, devices, and / or systems capable of detecting undesirable particles in the inhaled air that pass through the filters 122, 124 due to particle size, filter imperfections or defects (due to tears, holes, improper installation, and / or the recrystallization process), filter manufacturing defects, and / or operational wear.

[0034] In many embodiments, the system 100 may include an electrostatic component 126 (e.g., a matrix of ionizers) disposed in the inlet section 12 (e.g., within the filter chamber 120). For example, the electrostatic component 126 may be disposed in the inlet section 12 downstream of the array of fabric filters 124. The electrostatic component 126 may be configured to charge particles passing through it, such that the particles retain an electrostatic charge, making them easier to detect by a sensing system. Charged particles contained in the intake air may enable easier and / or improved detection of particles before they reach the compressor section 14 of the gas turbine 10. In an exemplary embodiment, the particle sensor(s) 112A, 112B, 112C, 112D described herein below are capable of detecting naturally charged particles without the presence of the electrostatic component 126.

[0035] As shown in FIG. 1 , in many embodiments, system 100 can include particulate sensor 112A, particulate sensor 112B, particulate sensor 112C, and particulate sensor 112D, each in communication with controller 200. Each particulate sensor 112 can be configured to sense data indicative of one or more particulate parameters and provide the data to controller 200. The particulate parameters can be parameters related to particulates entering or exiting the gas turbine from / to the atmosphere (or surrounding environment). For example, in an exemplary implementation of system 100, gas turbine 10 can be located on the border of a desert. In such an implementation, the particulate parameter can be associated with sand particles entering or exiting the gas turbine 10. For example, the sensed particulate parameter can be particulate type, size, weight, or quantity (e.g., volume of particulates per volume unit of air). In many embodiments, particulate sensor(s) 112 can be configured to sense data indicative of particulate weight, particulate volume, particulate density, particulate type (e.g., sand, dust, etc.), particulate count type, particulate quantity, particulate size, and / or other data indicative of particulate parameters.

[0036] Each of particle sensors 112A, 112B, 112C, and 112D is operatively coupled to and / or in operative communication (e.g., electronically) with controller 200. Particle sensors 112A, 112B, 112C, and 112D may be located downstream of a filtration stage (e.g., filters 122, 124). In certain embodiments, particle sensors 112A, 112B, 112C, and 112D may be electrostatic and may be naturally charged or pre-charged by electrostatic component 126 and configured to detect charged particles in the intake air flowing past particle sensors 112A, 112B, 112C, and 112D. In some embodiments, each of particle sensors 112A, 112B, 112C, and 112D may be formed as a flush-mounted button sensor with high local resolution, a multiple button system sensor arranged in a ring, a circumferential ring sensor, etc. Additionally or alternatively, particle sensors 112A, 112B, 112C, and 112D may be staged in the flow direction to increase the detectability of charged particles entrained by the flow by correlating signals of different stages with flow velocity known to controller 200. It should be understood that the location(s) and number of particle sensors 112A, 112B, 112C, and 112D shown in the embodiments may vary, and system 100 may include more or fewer than shown in the figures.

[0037] During operation of the gas turbine system 10, the intake air 105 may flow through the filters 122 and 124 to provide a working fluid (e.g., filtered air 107) to the compressor section 14. Some particles contained in the intake air 105 may flow through the filters 122 and 124. The particles may be detected by the particulate sensors 112A, 112B, 112C, and 112D. For example, the particulate sensors 112A, 112B, 112C, and 112D may detect ingested particles and provide information to the controller 200.

[0038] In an exemplary embodiment, the particulate sensor 112A may be disposed at the inlet of the gas turbine 10. For example, the particulate sensor 112A may be disposed at the inlet 15 of the compressor section 14 to measure data indicative of one or more particulate parameters entering the compressor section 14. Referring briefly to FIG. 2 , a cross-sectional view of the inlet 15 to the compressor section 14 is shown in accordance with an embodiment of the present disclosure. As shown, the compressor section 14 may include an outer casing 114, a shaft 22, and a plurality of rotor blades 116 extending radially between the shaft 22 and the outer casing 114. A compressor flowpath 118 may be defined between the outer casing 114 and the shaft 22, into which air from the filter chamber 120 enters. The particulate sensor 112A may be a plurality of particulate sensors 112A circumferentially spaced (e.g., equally spaced) from one another and disposed in the compressor section 14. For example, multiple particulate sensors 112A may be positioned on the outer casing 114 and within the flow path 118 such that the particulate sensors 112 may measure data indicative of particulate parameters at the inlet 15 of the compressor section 14 .

[0039] 1 , the system 100 may further include a particulate sensor 112B disposed at the outlet of the gas turbine 10. For example, the particulate sensor 112B may be disposed at the outlet 19 of the turbine section 18 to measure data indicative of one or more particulate parameters exiting the turbine section 18. In many embodiments (not shown), the particulate sensor 112B may be a plurality of particulate sensors 112B disposed on the outer casing of the turbine section 18 and spaced circumferentially from one another to measure data indicative of the particulate parameters at the outlet 19 of the turbine section 18. In contrast to the particulate sensor 112A at the inlet 15 of the compressor section, which measures particulate parameters associated with particulate inflow (e.g., sand, dirt, dust, etc.), the particulate sensor 112B may measure particulate parameters associated with particulate outflow (which may include sand, dirt, dust, and soot associated with incomplete consumption of fuel in the combustion section). In some implementations, if sensor 112A does not measure particulate inflow into compressor section 14 (e.g., no sand, dirt, dust, etc. entering the gas turbine), but sensor 112B measures particulate outflow from turbine section 18, controller 200 can determine that incomplete consumption of fuel is occurring in combustion section 16, resulting in soot exiting turbine section 18 (and potentially accumulating in turbine section 18 affecting efficiency). In response, controller 200 can adjust the amount of fuel (e.g., increase or decrease fuel from the fuel supply) or change the type of fuel (e.g., gas or liquid) to reduce soot generation and create a more complete combustion in the combustion section.

[0040] Additionally, in many embodiments, system 100 may further include one or more particulate sensors 112C disposed within inlet section 12, such as within a filter chamber, to measure data indicative of particulate parameters within inlet section 12. Additionally, in various embodiments, system 100 may further include one or more particulate sensors 112D disposed in an exhaust section, such as an exhaust stack, to measure data indicative of particulate parameters within exhaust section 20.

[0041] Further, in many embodiments, system 100 may include compressor sensors 136, combustor sensors 138, and turbine sensors 140, each in operative communication with controller 200. Compressor sensor 136 may be located in compressor section 14 and configured to measure data indicative of one or more parameters related to compressor section 14. For example, compressor sensor 136 may be configured to measure and provide data indicative of pressure, temperature, air flow rate, compressor rotational speed, or other data related to compressor section 14. Combustor sensor 138 may be located in combustion section 16 and configured to measure and provide data related to combustion section 16. For example, combustor sensor 138 may be configured to provide data indicative of combustion gas pressure, temperature, flow rate, combustion gas composition, fuel type, or other data related to combustion section 16. Turbine sensor 140 may be located in turbine section 18 and configured to measure data indicative of one or more parameters related to turbine section 18. For example, turbine sensor 140 may be configured to measure and provide data indicative of pressure, temperature, combustion gas flow rate, turbine rotational speed, or other data associated with turbine section 18. Additionally, the controller may utilize any of the sensed data from compressor sensor 136, combustor sensor 138, and turbine sensor 140, along with data indicative of one or more particulate parameters from particulate sensor 112A, particulate sensor 112B, particulate sensor 112C, and particulate sensor 112D, to determine fouling, deposition, and deterioration rates associated with one or more components of gas turbine 10.

[0042] In certain embodiments, system 100 may include one or more filter chamber sensors 142, each in operative communication with controller 200. Filter chamber sensors 142 may be located on either side of array of fabric filters 124. In such embodiments, filter chamber sensors 142 may each be configured to sense and provide data indicative of pressure to controller 200. Controller 200 may compare the data indicative of pressure from each filter chamber sensor 142 to determine a pressure differential across array of fabric filters 124. Controller 200 may monitor the pressure differential across array of fabric filters 124 to determine the health and remaining life of fabric filters 124. Furthermore, the pressure differential across fabric filters 124, along with data indicative of one or more particulate parameters from particulate sensors 112A, 112B, 112C, and 112D, may be utilized by controller 200 to determine fouling, deposition, and degradation rates associated with one or more components of gas turbine 10.

[0043] In various embodiments, the system 100 may further include an environmental sensor 128 disposed outside the gas turbine 10, which may be communicatively coupled to the controller 200 and configured to provide data indicative of severe weather conditions. The data indicative of severe weather conditions may include ambient temperature, ambient pressure, specific and relative humidity, and / or wind speed. Additionally, the data indicative of severe weather conditions may include parameters related to particulates in the ambient environment surrounding the gas turbine 10, such as the amount of particulates in the air (which increases / decreases with the weather conditions), the type of particulates in the air (e.g., sand, dust, dirt, etc.), the size of the particulates in the air, and other parameters. The controller 200 may implement a control action related to the wash system 102 based on the data indicative of severe weather conditions from the environmental sensor 128.

[0044] In the exemplary embodiment, the gas turbine 10 may further include an extraction cooling pipe 130 extending between the compressor section 14 and the turbine section 18. For example, the extraction cooling pipe 130 may extend from an inlet disposed on and in fluid communication with an outer casing of the compressor section 14 to an outlet disposed on and in fluid communication with one or more components of the turbine section 18 (such as turbine rotor blades and / or turbine stator vanes). The extraction cooling pipe 130 may be configured to convey bleed air from the compressor section 14 to the turbine section 18 for use with (e.g., for cooling) the one or more turbine components. A contamination sensor 132 may be disposed in the extraction cooling pipe 130. The contamination sensor 132 may be configured to communicate with the controller 200 and provide the controller with data indicative of particulate contamination in the bleed air. The data indicative of particulate contamination in the bleed air may include an amount of particulates in the bleed air, a size of the particulates in the bleed air, a type of particulates in the bleed air (e.g., sand, dust, dirt, etc.), and other particulate parameters. The controller 200 may implement control actions associated with the washing system 102 based on data indicative of particulate contamination in the bleed air.

[0045] In many embodiments, the system 100 may include a fuel supply system 134 configured to supply fuel to the combustion section 16. For example, the fuel supply system 134 may include a fuel supply source 137 and one or more fuel lines extending between the fuel supply source 137 and the combustion section 16. The fuel supply system may be configured to supply gaseous fuel and / or liquid fuel to the combustion section 16.

[0046] In many embodiments, the controller 200 may include one or more models 202 (e.g., stored in a memory and executable by a processor), and the controller 200 may provide data indicative of one or more particulate parameters from the particulate sensor(s) 112 to the one or more models 202. Additionally, the controller 200 may provide data from the compressor sensors 136, the combustor sensors 138, and / or the turbine sensors 140. In some embodiments, the models 202 may be constructed by the controller 200 (or may be stored in a memory of the controller 200). The models 202 may evaluate measurement data indicative of one or more particulate parameters provided by the particulate sensor(s) 112 (as well as other sensors in the system 100) to determine fouling, deposition, and deterioration rates associated with one or more components of the gas turbine.

[0047] In some embodiments, the sensor data may be provided to a data management system 204, such as an inlet monitoring system or an outlet monitoring system. The data management system 204 may be stored in a memory of the controller 200 and executable by a processor of the controller 200. Alternatively, the data management system 204 may be a stand-alone computing system. The data management system 204 may filter noise and reduce errors from the sensor data. The data management system 204 may receive data indicative of one or more particulate parameters (as well as data from other sensors in the system 100) as inputs and provide the velocity, average size, volume, type, distribution, and / or dispersion pattern of particulates entering and exiting the gas turbine 10 as outputs to the one or more models 202. In some embodiments, the inlet and outlet monitoring systems may be models stored in a memory (and / or executable by a processor) of the controller 200. In other embodiments, the inlet and outlet monitoring systems may be stand-alone computing systems that may receive data indicative of one or more particulate parameters as inputs and provide the velocity, average size, volume, type, distribution, and / or dispersion pattern of particulates entering and exiting the gas turbine 10 as outputs.

[0048] The one or more models 202 may include a set of equations and algorithms that allow the controller 200 to analyze the measured data to determine deposition rates, fouling rates, and / or degradation rates.

[0049] In one embodiment, the model 202 determines the total contaminant level ("TCL" in parts per million by weight, hereafter "ppmw") according to the following formula: TCL=I f +[I air ×A / F]+[I w ×W / F]+[I stm ×S / F] In the formula, I f is the contaminant level in the fuel (ppmw), I airis the pollutant level in the air (ppmw), I w is the contaminant level in the injection water (ppmw), I stm is the contaminant level in the injected steam (ppmw), A / F is the air-to-fuel ratio of the gas turbine, S / F is the steam-to-fuel ratio, and W / F is the water-to-fuel ratio. The particulate behavior is captured by the Stokes number, St, During the ceremony,

number

number

[0050] In an embodiment, the air flow in the tube, which indicates the particle flux I (i.e., flow of particles per surface area and time) to the tube wall, is described by:

number

[0051] A fouling relationship exists that combines the geometric and aero-thermal properties of the compressor section 14. This relationship is derived by considering the cylinder entrainment efficiency due to inertial deposition corrected for the airfoil row entrainment efficiency due to inertial deposition, i.e., the following equation:

number

number

[0052] The collection efficiency is inversely proportional to the particle size and the flow velocity: the smaller the particles and the slower the airflow, the higher the deposition rate. For an airfoil of chord length L, the collection efficiency for the diffusion process is:

number

number

number

number

[0053] The model 202 utilizes data indicative of one or more particulate parameters from the particulate sensor(s) 112 and / or the data management system 204 to determine fouling, deposition, and degradation rates associated with one or more components of the gas turbine. Additionally, or alternatively, the model 202 utilizes data indicative of one or more particulate parameters from the particulate sensor(s) 112 and / or the data management system 204 to determine a total cumulative fouling load, estimate fouling load over time, and / or estimate filtration efficiency. Furthermore, the model 202 may predict degradation rates, fouling, and / or deposition rates. In response to the prediction, the controller 200 may adjust or modify a wash schedule to minimize downtime of the gas turbine 10. The determined total cumulative fouling load enables the model 202 to establish a remaining life of the gas turbine 10 relative to firing time and fouling load. If the estimated dirt load over time is too high (e.g., above a predetermined threshold), the model 202 may recommend and / or command online cleaning, increase the cleaning pulse frequency to remove more grit / dirt from the component, etc., until the condition is met.

[0054] 3, a block diagram of a system 100 is shown in accordance with an embodiment of the present disclosure. As shown, the system 100 includes a controller 200 and one or more sensor(s) 201 in operative communication with the controller 200 and configured to monitor one or more operating parameters of the gas turbine 10. The sensor(s) 201 may include any of the sensors described above with reference to FIG. 1, such as the particulate sensor 112A, the particulate sensor 112B, the particulate sensor 112C, the particulate sensor 112D, the compressor sensor 136, the combustor sensor 138, the turbine sensor 140, the filter house sensor 142, etc.

[0055] 3 , controller 200 is shown as a block diagram to illustrate suitable components that may be included within controller 200. As shown, controller 200 may include one or more processor(s) 206 and associated memory device(s) 208 configured to perform various computer-implemented functions (e.g., executing methods, steps, calculations, etc., and storing associated data as disclosed herein). Additionally, controller 200 may also include a communications module 210 that facilitates communication between controller 200 and various components of system 100. For example, communications module 210 may communicate with gas turbine 10 and washing system 102 to enable processor 206 to selectively initiate online washing or shut down gas turbine 10 for offline washing. Additionally, communications module 210 may include a sensor interface 212 (e.g., one or more analog-to-digital converters) that enable signals transmitted from one or more sensors 201 to be converted into signals that can be understood and processed by processor 206. It should be understood that sensor(s) 201 may be communicatively coupled to communications module 210 using any suitable means. For example, the sensor(s) 201 may be coupled to the sensor interface 212 via a wired connection. However, in other embodiments, the sensor(s) 201 may be coupled to the sensor interface 212 via a wireless connection, such as by using any suitable wireless communication protocol known in the art. Additionally or alternatively, one or more of the sensor(s) 201 may be communicatively coupled to the data management system 204, which may in turn be coupled to the controller 200.

[0056] As used herein, the term "processor" refers not only to integrated circuits referred to in the art as being included in a computer, but also to controllers, microcontrollers, microcomputers, programmable logic controllers (PLCs), application specific integrated circuits, and other programmable circuitry. Additionally, memory device(s) 208 may generally comprise memory element(s) including, but not limited to, computer-readable medium (e.g., random access memory (RAM)), computer-readable non-volatile media (e.g., flash memory), floppy disks, compact disk read-only memory (CD-ROM), magneto-optical disks (MODs), digital versatile disks (DVDs), and / or other suitable memory elements. In such memory device(s) 208, generally, suitable computer-readable instructions may be configured to store suitable computer-readable instructions that, when executed by the processor(s) 206, configure the controller 200 to perform various functions and / or operations, including, but not limited to, providing data indicative of one or more particulate parameters from a particulate sensor to one or more models 202 (which may be stored in the memory device 208); using the one or more models 202 to determine at least one of a fouling rate, deposition rate, and deterioration rate associated with one or more components of the gas turbine 10 based on the one or more particulate parameters; and implementing a control action associated with the washing system 102 based on the magnitude of at least one of the fouling rate, deposition rate, and deterioration rate.

[0057] 2 , the controller 200 may receive gas turbine operational data 214 and environmental data 216 as additional inputs. The gas turbine operational data 214 and the environmental data 216 may be utilized by one or more model(s) to determine fouling, deposition, and deterioration rates associated with one or more components of the gas turbine 10. The environmental data may include wind speed at the location of the gas turbine, a dust or sandstorm index, a concentration of particulates (e.g., sand, dust, dirt, etc.) in the ambient air, an ambient temperature, and / or an ambient pressure. The gas turbine operational data 214 may include an IGV angle, a compressor inlet temperature, a compressor air flow rate, a compressor efficiency, a compressor discharge parameter, a combustion gas temperature and / or pressure, and other turbine operational data. The environmental data 216 and the gas turbine operational data 214 may be provided to the controller 200 in real time.

[0058] In many embodiments, the controller 200 may utilize a physics-based engine model 218 (e.g., a “digital twin” model) of the gas turbine 10. The physics-based engine model 218 may virtually represent the state of the gas turbine 10. The physics-based engine model 218 may include parameters and dimensions of its physical twin's parameters and dimensions that provide measurements and keep the values ​​of those parameters and dimensions current by receiving and updating values ​​via output from sensors integrated into the physical twin. The digital twin may have respective virtual components corresponding to essentially every physical and operating component of the gas turbine 10.

[0059] The physics-based engine model 218 may be stored in the memory 208 of the controller 200 and may be executable by the processor 206. Generally, the physics-based engine model 218 may provide one or more inputs, and the engine digital twin model may generate one or more outputs based, at least in part, on the one or more inputs. For example, the physics-based engine model 218 may operate in conjunction with (i.e., side-by-side or together with) one or more models 202 such that the physics-based engine model 218 can provide gas turbine operating data 214 to the one or more models 202. The physics-based engine model 218 may be used to verify or validate sensed data from one or more sensors 201.

[0060] 3 , the controller 200 may determine a particulate inflow level, fouling rate, deposition rate, and degradation rate associated with the particulate inflow of the gas turbine 10 based on data received from one or more sensors 201, environmental data 216, gas turbine operating data 214, and / or data from a physics-based engine model 218. Based on the particulate inflow magnitude, fouling rate, deposition rate, and degradation rate associated with the particulate inflow of the gas turbine 10, the controller 200 may initiate one or more control actions 222 associated with the wash system 102 and / or the gas turbine 10. For example, the one or more control actions 222 may include initiating an online wash of the gas turbine 10 with the wash system 102, shutting down the gas turbine 10 for offline washing, and / or optimizing a cleaning agent (or detergent) mixture. Optimizing the cleaning agent (or detergent) mixture may include adjusting the amount (or concentration) of cleaning agent (or detergent) mixed with water based on the intensity of the cleaning required. For example, if the magnitude of at least one of the soiling rate, accumulation rate, and degradation rate is high, the amount (or concentration) of detergent in the cleaning solution can be increased.

[0061] The controller 200 may also be configured to generate a notification signal if the controller 200 determines that the magnitude of at least one of the fouling rate, deposition rate, and degradation rate exceeds one or more thresholds. Thus, as shown in FIG. 3 , in one example, the controller 200 may be configured to send a notification signal to a user, for example, via the user interface 220. The notification signal may be associated with the washing system 102, such as an advisory for an impending cleaning (i.e., a notification for an upcoming cleaning based on the fouling rate, deposition rate, and / or degradation rate) or a notification regarding an offline cleaning (i.e., the gas turbine 10 will be shut down for offline cleaning). Notifications may also be associated with the gas turbine 10, such as an advisory for an inspection of the filter house 120 or a notification regarding the impact of heavy fuel operation (i.e., fuel economy being affected as a result of particulate influx).

[0062] 4, a real-time degradation / fouling recommendation algorithm framework 300 is provided in accordance with an exemplary embodiment of the present disclosure. The real-time degradation / fouling recommendation algorithm framework 300 may be stored in the memory device 208 and executable by the processor 206. The real-time degradation / fouling recommendation algorithm framework 300 may form part of the model 202, be utilized by the model 202, and / or be referenced by the controller 200. The model 202 may utilize the real-time degradation / fouling recommendation algorithm framework 300 to analyze sensory data and evaluate data related to the compressor section 14 (e.g., compressor air flow rate, compressor efficiency, etc.), inlet filtration design parameters, filtration efficiency, inlet filtration differential pressure (“DP”), run time, debris monitoring, and / or debris composition (e.g., Si, Ca, S, Fe, etc.). Using the real-time degradation / fouling advisory algorithm framework 300, the model 202 can compare actual compressor efficiency with predicted (or estimated) compressor efficiency to determine airflow loss, compressor degradation and power loss (or MW loss), and exhaust diffusion.

[0063] Further, using the real-time degradation / fouling recommendation algorithm framework 300, the model 202 can determine a degradation pattern based on particulate size and accumulation in the compressor over continued operation. For example, the compressor degradation pattern can include periods of high compressor degradation and periods of low compressor degradation. The controller 200 can initiate online washing of the compressor section 14 during periods of high compressor degradation. Further, using the real-time degradation / fouling recommendation algorithm framework 300, the model 202 can determine a predicted particulate count, volume, and / or distribution of particulates entering the gas turbine 10. Similarly, the model 202 can determine a predicted particulate size and volume behavior (or impact) on erosion, corrosion, and / or fouling of the compressor section 14 and / or turbine section 18. For example, if the predicted particulate size / volume is less than 10 microns, the model 202 can determine that the compressor section 14 and / or turbine section 18 are experiencing increased fouling. Furthermore, if the expected particulate size / volume is greater than 10 microns, the model 202 may determine that the compressor section 14 and / or the turbine section 18 are experiencing increased erosion. Thus, the controller 200 may initiate one or more control actions related to the washing system (e.g., online wash timing, offline wash schedule, etc.) in response to determining the expected particulate size / volume to account for fouling / erosion. Furthermore, the controller 200 may adjust the deterioration rate to reflect the detected inflow data, and based on the adjustment to the deterioration rate (i.e., the new magnitude of the deterioration rate), the controller 200 may adjust the washing schedule of the gas turbine 10, initiate online washing, or shut down the gas turbine for offline washing.

[0064] 5 , a flow chart of a model 500 (such as model 202 described above or a different model) that may be utilized by controller 200 to monitor and respond to particulate influx within gas turbine 10 is shown. Model 500 may provide input 508, which may be processed with one or more algorithms, sets of equations, look-up tables, graphs, and / or outputs 510 to determine an output 510. Based on the output 510, controller 200 may implement one or more control actions associated with wash system 102. Input 508 to model 500 may include gas turbine operating data 502, sensor data 504 (e.g., from one or more of the sensors described above with reference to FIG. 1 ), and / or data from a physics-based digital model 506. Input 508 may include ambient temperature, ambient pressure, specific and relative humidity, inlet guide vane angle, compressor inlet temperature, compressor air flow rate, compressor efficiency, engine corrosion parameters, compressor performance variations, combustion gas temperature, exhaust temperature, and / or exhaust pressure.

[0065] The outputs 510 of the model 500 can be calculated sequentially, with each output 510 calculated using information from previously calculated outputs. The outputs 510 from the model 500 can include compressor efficiency during operation (e.g., real-time compressor efficiency), particulate type (e.g., sand, dust, dirt, etc.), distribution, and volume. The outputs 510 from the model 500 can further include fouling rate, deposition rate, and degradation rate. The model 500 can further determine the effects of compressor efficiency loss (e.g., resulting from the sensed particulate influx), airflow loss, and compressor performance degradation. Once the compressor efficiency loss, airflow loss, and compressor performance degradation are calculated by the controller 200 using the model 500, the controller 200 can further determine the power impact (e.g., MW impact), thermal efficiency penalty, and operation and maintenance impact. Finally, the model 500 can determine the estimated electrical impact on the gas turbine as a result of particulate influx and / or the estimated fuel impact on the gas turbine as a result of particulate influx. Additionally, model 500 can determine whether an unplanned outage is necessary, the downtime associated with such an unplanned outage, and the financial impact associated with such an unplanned outage.

[0066] 5 , in some embodiments, model 500 can provide economic impact 512 as a result of particulate influx, which can include lost revenue due to power and / or performance impacts, lost revenue due to inefficient fuel consumption, etc. Model 500 can further provide cycle time impact 514 associated with maintenance actions required to mitigate the impact of particulate influx on gas turbine 10, such as downtime (dollars per day) associated with offline cleaning, downtime (dollars per day) associated with filter house inspections, or shutdowns associated with other maintenance actions. Additionally, cycle time impact 514 can include an estimate of downtime associated with an erosion / corrosion risk parameter generated based on the particulate influx, deposition rate, degradation rate, and fouling rate. If the risk parameter exceeds a risk threshold, controller 200 can initiate online cleaning, adjust one or more gas turbine operating parameters, adjust the detergent mixture in the cleaning fluid, and / or shut down the gas turbine for offline cleaning.

[0067] Based on the output 510, the economic impact 512, and / or the cycle time impact 514, the controller 200 may initiate one or more control actions to mitigate downtime of the gas turbine 10. For example, if high particulate inflow is detected and / or environmental data indicates a large amount of dust, dirt, sand, or other particulates, the controller 200 may schedule and / or initiate an online wash of the gas turbine 10 by the washing system 102. Additionally, the controller may generate a notification (which may be provided to the user interface 220) that an online wash is imminent.

[0068] If the output 510 of the model 500 indicates that the gas turbine 10 is experiencing a high particulate inflow (e.g., greater than a first threshold), a buildup of particulate deposits, and / or that the washing system 102 has been repeatedly used for online cleaning (due to the high particulate inflow), the controller 200 may generate an alarm or notification (which may be provided to the user interface 220) that an inspection of the filter house 120 is necessary. For example, the controller 200 may predict that the high particulate inflow is due to a filter house failure (i.e., one or more of the filters has torn or broken such that the filters are no longer active), and the controller 200 may provide a notification that the filter house 120 may be damaged. Additionally or alternatively, if the model 500 indicates that the gas turbine 10 is experiencing a very high particulate inflow (e.g., greater than a second threshold, a second threshold greater than the first threshold) and / or a buildup of particulate deposits, the controller 200 may generate a recommendation as to when to shut down the gas turbine 10. The recommendation regarding the timing of shutting down the gas turbine 10 may minimize the economic impact and / or cycle time impact as a result of offline washing (e.g., the shutdown time may be during a period of low power load). In some embodiments, the controller 200 may determine the type and / or optimal mixture of detergent for the washing fluid used in offline washing based on the magnitude of the fouling rate, deposition rate, degradation rate, and / or other parameters related to particulate inflow in the gas turbine 10.

[0069] During operation, by continuously monitoring the particulate inflow of the gas turbine 10 and the fouling, deposition, and degradation rates of the gas turbine as a result of the particulate inflow, the controller 200 can minimize the number of offline washes required (thereby minimizing downtime) by addressing the particulate inflow with online washes in a timely manner.

[0070] 6, a block diagram of one or more aspects of a model 500 for monitoring and responding to particulate inflow within a gas turbine 10 is shown, in accordance with an embodiment of the present disclosure. As shown, model 500 may include a particulate estimation model 516, a maintenance factor estimation model 518, and a gas turbine engine control 520. Particulate estimation model 516, maintenance factor estimation model 518, and gas turbine engine control 520 may operate together to generate one or more control actions 522 based on particulate inflow in the gas turbine 10. Particulate estimation model 516 may be provided with one or more inputs (e.g., from one or more sensors described above with reference to FIG. 1 ), including, but not limited to, particulate size, particulate volume, particulate type, whether the wash system 102 is on or off, accumulated inflow, and exhaust debris monitoring data. The maintenance factor estimation model 518 may be provided with one or more inputs including, but not limited to, an erosion / deposit transfer function, a deposit location estimator, a temperature profile for each stage (e.g., compressor blade or turbine blade), collection efficiency, particulate impact, a fouling rate estimator, and / or total contamination level. The gas turbine engine controls 520 may include and / or provide one or more inputs including, but not limited to, ambient temperature, compressor discharge parameters, turbine efficiency, gas turbine airflow, IGV angle, inlet filter pressure differential, performance monitoring data, compressor efficiency, firing temperature, exhaust temperature, and / or exhaust temperature spread.

[0071] In many embodiments, the controller 200 may initiate one or more control actions 522 associated with the washing system 102 and / or the gas turbine 10 based on the calculations of the model 500. For example, the one or more control actions 522 may include initiating an online wash of the gas turbine 10 with the washing system 102, shutting down the gas turbine 10 for offline washing, and / or optimizing a cleaning agent (or detergent) mixture. Optimizing the cleaning agent (or detergent) mixture may include adjusting the amount (or concentration) of cleaning agent (or detergent) mixed with water based on the intensity of the cleaning required. For example, if the magnitude of at least one of the soiling rate, deposition rate, and degradation rate is high, the amount (or concentration) of detergent in the washing solution may be increased.

[0072] 7, a logic flow chart 700 is shown in accordance with one or more exemplary aspects of the present disclosure. Logic flow chart 700 may be followed by steps in which controller 200 determines when to perform an online wash, an offline wash, or continue normal operation of gas turbine 10. As shown, logic flow chart 700 may take into account data from environmental sensors 128, particulate sensors 112 (e.g., particulate sensor 112A, particulate sensor 112B, particulate sensor 112C, particulate sensor 112D), and contamination sensor 132 in extraction cooling pipe 130.

[0073] At decision block 702, the controller may determine whether data from the environmental sensors 128 indicates abnormal ambient conditions (e.g., whether wind speed, ambient temperature, ambient pressure, humidity, etc. are abnormally high / low based on historical data or other considerations). If not, the gas turbine 10 may continue normal operation, as indicated by block 704. As indicated by blocks 706 and 708, if the outside ambient conditions are abnormal, the controller 200 may determine whether the data indicating the severe weather conditions is greater than a first severe weather threshold and / or a second severe weather threshold. The second severe weather threshold may be greater than the first severe weather threshold. The severe weather threshold may include thresholds for one or more weather-related parameters, such as wind speed, ambient temperature, ambient pressure, humidity, the amount of particulates in the ambient environment, the size of particulates in the ambient environment, or other weather-related parameters. If the data indicating the severe weather conditions is greater than the first severe weather threshold (but not the second severe weather threshold), the controller 200 may send a signal to the washing system 102 to initiate an online wash (as indicated by block 710). If the data indicating the severe weather conditions is greater than the second severe weather threshold, the controller 200 may shut down the gas turbine 10 for an offline wash (as indicated by block 712).

[0074] At decision block 714, the controller may determine whether data from the particulate sensor 112 indicates particulate inflow in the gas turbine (or particulate outflow from the gas turbine, as the case may be). If not, the gas turbine 10 may continue normal operation, as indicated by block 704. If particulate inflow is detected (or particulate outflow, as the case may be), as indicated by blocks 716 and 718, the controller 200 may determine whether the particulate inflow level is greater than a first inflow threshold and / or a second inflow threshold. The second inflow threshold may be greater than the first inflow threshold. If the data indicating particulate inflow is greater than the first inflow threshold (but not the second inflow threshold), the controller 200 may send a signal to the washing system 102 to initiate online washing (as indicated by block 710). If the data indicating particulate inflow is greater than the second inflow threshold, the controller 200 may shut down the gas turbine 10 for offline washing (as indicated by block 712).

[0075] At decision block 720, the controller may determine whether data from the contamination sensor 132 indicates particulate contamination in the bleed air conveyed through the extraction cooling pipe 130. If not, the gas turbine 10 may continue normal operation, as indicated by block 704. If particulate contamination is detected in the bleed air, as indicated by blocks 722 and 724, the controller 200 may determine whether the particulate contamination level is greater than a first contamination threshold and / or a second contamination threshold. The second contamination threshold may be greater than the first contamination threshold. If the data indicative of particulate contamination is greater than the first contamination threshold (but not the second contamination threshold), the controller 200 may send a signal to the washing system 102 to initiate online washing (as indicated by block 710). If the data indicative of particulate contamination is greater than the second contamination threshold, the controller 200 may shut down the gas turbine 10 for offline washing (as indicated by block 712).

[0076] Referring now to FIG. 8 , a flow diagram of one embodiment of a method 800 for timely addressing particulates in a gas turbine, in accordance with an aspect of the present inventive subject matter, is shown. Generally, the method 800 is described herein with reference to the gas turbine 10 and system 100 described above with reference to FIGS. 1-7 . However, those skilled in the art will understand that the disclosed method 800 may generally be utilized with any suitable gas turbine and / or may be utilized in connection with a system having any other suitable system configuration. Furthermore, while FIG. 8 depicts steps performed in a particular order for purposes of illustration and description, the methods described herein are not limited to any particular order or arrangement unless otherwise specified in the claims. Those skilled in the art, using the disclosure provided herein, will understand that various steps of the methods disclosed herein may be omitted, rearranged, combined, and / or adapted in various ways without departing from the scope of the present disclosure.

[0077] In an exemplary embodiment, method 800 includes, at (802), using controller 200 to monitor data indicative of one or more particulate parameters using particulate sensor 112. Particulate sensor 112 may be located at at least one of the inlet 15 to compressor section 14 or the outlet 19 of turbine section 18. The particulate parameters may include type of particulate (e.g., sand, dirt, dust, etc.), size of the particulate, amount (or concentration) of particulate entering compressor section 14 (e.g., amount of particulate present in air entering compressor section 14), and amount (or concentration) of particulate exiting turbine section 18 (e.g., amount of particulate present in exhaust gas exiting turbine section 18).

[0078] In many implementations, the method 800 can further include, at 804, using the controller 200, determining when the data indicative of one or more particulate parameters exceed a particulate threshold. The particulate threshold can include thresholds for each of the particulate parameters (e.g., a particulate type threshold, a particulate size threshold, a particulate amount threshold), and the controller 200 can determine that only one of the particulate parameters exceeds the particulate size threshold and the other does not.

[0079] In an exemplary embodiment, method 800 may further include, at 806, implementing a control action associated with the washing system 102 in response to determining that the data indicative of one or more particulate parameters exceeds a particulate threshold. For example, the one or more control actions may include initiating an online wash of the gas turbine 10 with the washing system 102, shutting down the gas turbine 10 for offline washing, and / or optimizing a cleaning agent (or detergent) mixture used in online and / or offline washing. Optimizing a cleaning agent (or detergent) mixture may include adjusting the amount (or concentration) of cleaning agent (or detergent) mixed with water based on the intensity of the cleaning required. Accordingly, the control action may include adjusting the concentration of detergent in the washing solution used by the washing system.

[0080] In many embodiments, depending on how often the system 100 performs online water washes in response to particulate parameters exceeding a threshold, the washing system 102 can increase the concentration of detergent in the wash fluid to increase the effectiveness of each online wash and decrease the frequency of the online washes. For example, if the frequency of online washes exceeds a frequency threshold, the washing system 102 can increase the concentration of detergent in the wash fluid used by the washing system. Additionally or alternatively, if the frequency of online washes exceeds a frequency threshold, the controller can generate a notification that the compressor needs to be inspected due to excessive soiling (e.g., the compressor's finish may be significantly damaged).

[0081] In many embodiments, the control action may include adjusting a wash schedule for the gas turbine 10 to minimize downtime of the gas turbine 10. The wash schedule may include predetermined dates and times when the gas turbine is to undergo an online wash or is to be shut down for an offline wash. The control action may include adjusting one or more of the predetermined dates and times when the gas turbine is to undergo an online wash or is to be shut down for an offline wash based on one or more detected particulate parameters. In many embodiments, the control action may include adjusting (e.g., increasing) the duration of the next online wash based on the sensed particulate parameters.

[0082] In various embodiments, the method may include generating a notification indicating that maintenance action is required for the gas turbine. The notification may be generated in response to one or more sensed particulate parameters. The maintenance action may include a filter house inspection, an offline water wash, replacement of a component of the gas turbine, etc. For example, if the controller determines that particulate inflow is increasing and / or that the pressure differential across the filters 122, 124 in the filter house 120 has decreased, the controller may generate a notification to inspect the filter house 120 for damage (i.e., failure) of one of the filters 122, 124.

[0083] In many embodiments, the method 800 may include a first particulate threshold and a second particulate threshold, where the second particulate threshold may be greater than the first particulate threshold, and the controller 200 may determine the severity of the required control action based on where the sensed data indicative of the one or more particulate parameters falls relative to the first and second particulate thresholds. For example, if the controller determines that the data indicative of the one or more particulate parameters exceeds the first particulate threshold (but is below the second particulate threshold), the controller 200 may perform an online wash with the washing system 102. Further, if the controller determines that the data indicative of the one or more particulate parameters exceeds the first and second particulate thresholds, the controller 200 may shut down the gas turbine 10 and perform (or schedule) an offline wash with the washing system 102.

[0084] Additionally, in some implementations, the determination in 804 may be over a predetermined time period, thereby filtering out outlier events (e.g., spikes in one or more particulate parameters). In such implementations, one or more particulate parameters must exceed a threshold for the entire time period in order for the controller to take control action in 806.

[0085] In some implementations, method 800 may further include using one or more models 202 to determine at least one of a fouling rate, a deposition rate, and a degradation rate associated with one or more components of the gas turbine based on the one or more particulate parameters. In such implementations, method 800 may include implementing a control action associated with the washing system based on a magnitude of the at least one of the fouling rate, the deposition rate, and the degradation rate. For example, controller 200 may include a first rate threshold and a second rate threshold for each of the fouling rate, the degradation rate, and the deposition rate. The first rate threshold may be less than the second rate threshold.

[0086] In many embodiments, method 800 may include determining, using the controller, when one of the fouling rate, deposition rate, or degradation rate exceeds a first rate threshold and is below a second rate threshold. In such embodiments, method 800 may include performing an online wash of the gas turbine with washing system 102 in response to determining that one of the fouling rate, deposition rate, or degradation rate exceeds the first rate threshold and is below the second rate threshold. Further, in an exemplary embodiment, method 800 may include determining, using controller 200, when one of the fouling rate, deposition rate, or degradation rate exceeds the first rate threshold and exceeds a second rate threshold. In response, the method may include shutting down the gas turbine and performing an offline wash of the gas turbine with the washing system.

[0087] In many embodiments, the environmental sensor 128 may be located outside the gas turbine 10, and the environmental sensor 128 may be communicatively coupled to the controller 200 and configured to provide data indicative of the severe weather conditions. In such embodiments, the method 800 may further include implementing a control action associated with the wash system 102 based on the data indicative of the severe weather conditions. The data indicative of the severe weather conditions may include parameters related to particulates in the ambient environment surrounding the gas turbine 10, such as the amount of particulates in the air (which may increase or decrease with weather conditions), the type of particulates in the air (e.g., sand, dust, dirt, etc.), the size of the particulates in the air, and other parameters. The controller 200 may include a first severe weather threshold and a second severe weather threshold. The second severe weather threshold may be greater than the first severe weather threshold.

[0088] In an exemplary embodiment, method 800 may include determining, by controller 200, when data indicative of severe weather conditions exceeds a first severe weather threshold and is below a second severe weather threshold. In response, method 800 may include performing an online wash of the gas turbine with a wash system. Further, method 800 may include determining, by controller 200, when data indicative of severe weather conditions exceeds the first severe weather threshold and exceeds a second severe weather threshold. In response, the method may include shutting down the gas turbine performing an offline wash of the gas turbine with the wash system.

[0089] In various embodiments, the system 100 may include an extraction cooling pipe 130 extending between the compressor section 14 and the turbine section 18. For example, the extraction cooling pipe may fluidly couple the compressor section 14 and the turbine section 18. The extraction cooling pipe 130 may be configured to convey bleed air from the compressor section 14 to the turbine section 18 for use with one or more turbine components (e.g., to cool the turbine components). A contamination sensor 132 may be disposed within the extraction cooling pipe 130 and configured to provide data indicative of particulate contamination in the bleed air. In such embodiments, the method 800 may include implementing a control action associated with the washing system 102 based on the data indicative of particulate contamination in the bleed air. For example, the controller may include (e.g., stored in a memory) a first contamination threshold and a second contamination threshold. The second contamination threshold may be greater than the first contamination threshold.

[0090] In an exemplary embodiment, method 800 may include determining, with controller 200, when data indicative of particulate contamination in the bleed air exceeds a first contamination threshold and is below a second contamination threshold. In response, method 800 may include performing online washing of the gas turbine with washing system 102. Further, method 800 may include determining, with the controller, when data indicative of particulate contamination in the bleed air exceeds the first contamination threshold and is above a second contamination threshold. In response, method 800 may include shutting down gas turbine 10 and performing offline washing of the gas turbine with the washing system.

[0091] In many embodiments, the system 100 may include a particulate sensor 112A located at the inlet 15 of the compressor section 14 and a particulate sensor 112B located at the outlet of the turbine section 18. In some cases, the particulate sensor 112A may not measure particulate inflow into the compressor section 14 (e.g., sand, dirt, dust, etc. entering the gas turbine), but the sensor 112B measures particulate outflow from the turbine section 18. In such a case, the controller 200 may determine that incomplete consumption of fuel is occurring in the combustion section 16, resulting in soot exiting the turbine section 18 (and potentially accumulating in the turbine section 18 affecting efficiency). Alternatively or additionally, based on the particulate outflow from the turbine section 18 (e.g., soot exiting the turbine section 18), the controller 200 may determine that the fuel system 134 has been damaged (i.e., the fuel supply 137 and / or fuel line 139 has failed), and the controller 200 may generate an inspection notification (which may be provided to the user interface 220). For example, if the inlet monitoring system indicates that the gas turbine system is operating within normal parameters and the outlet monitoring system detects debris via the exhaust sensor, this could indicate a compromised / contaminated fuel system / damage.

[0092] The system 100 and method 800 described herein may advantageously reduce downtime of the gas turbine 10 by actively responding to particulate influx / flux within the gas turbine 10 using the wash system 102. For example, by addressing particulate influx / flux in the gas turbine 10 in a timely manner, the number of offline washes may be reduced (thereby reducing downtime), and the overall operating efficiency of the gas turbine 10 may be increased by ensuring that the gas turbine 10 is operating at full capacity without particulate buildup in the compressor section 14 and / or turbine section 18.

[0093] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any related methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they include structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements that have no substantial differences from the literal language of the claims.

[0094] Further aspects of the invention are provided by the subject matter of the following clauses.

[0095] A method for timely addressing particulates in a gas turbine, the gas turbine having a compressor section, a combustion section, and a turbine section, the method comprising: monitoring, with a controller, data indicative of one or more particulate parameters using a particulate sensor, the particulate sensor being located at at least one of an inlet to the compressor section or an outlet of the turbine section; determining, with the controller, when the data indicative of the one or more particulate parameters exceed a particulate threshold; and implementing a control action associated with a washing system in response to determining that the data indicative of the one or more particulate parameters exceed the particulate threshold.

[0096] The method of one or more of these clauses, further comprising: using one or more models to determine at least one of a fouling rate, a deposition rate, and a deterioration rate associated with one or more components of the gas turbine based on data indicative of one or more particulate parameters; and implementing a control action associated with the washing system based on a magnitude of the at least one of the fouling rate, the deposition rate, and the deterioration rate.

[0097] The method according to one or more of these clauses, further comprising: determining, by the controller, when one of the fouling rate, deposition rate, or degradation rate exceeds a first rate threshold and falls below a second rate threshold; and performing online washing of the gas turbine using a washing system in response to determining that one of the fouling rate, deposition rate, or degradation rate exceeds the first rate threshold and falls below the second rate threshold.

[0098] The method according to one or more of these clauses, further comprising: determining, by a controller, when one of the fouling rate, deposition rate, or degradation rate exceeds a first rate threshold and a second rate threshold; shutting down the gas turbine; and performing offline washing of the gas turbine using a washing system in response to determining that one of the fouling rate, deposition rate, or degradation rate exceeds the first rate threshold and the second rate threshold.

[0099] The method of one or more of these clauses, further comprising an environmental sensor disposed outside the gas turbine, the environmental sensor communicatively coupled to the controller and configured to provide data indicative of the severe weather condition, and the method further comprising implementing a control action associated with the washing system based on the data indicative of the severe weather condition.

[0100] The method of one or more of these clauses, further comprising: determining, by the controller, when the data indicative of the severe weather conditions exceeds a first severe weather threshold and is below a second severe weather threshold; and performing online washing of the gas turbine with the washing system in response to determining that the data indicative of the severe weather conditions exceeds the first severe weather threshold and is below the second severe weather threshold.

[0101] The method of one or more of these clauses, further comprising: determining, by the controller, when the data indicative of the severe weather conditions exceeds a first severe weather threshold and exceeds a second severe weather threshold; shutting down the gas turbine; and performing an offline wash of the gas turbine with a wash system in response to determining that the data indicative of the severe weather conditions exceeds the first severe weather threshold and exceeds the second severe weather threshold.

[0102] The method of any one or more of these clauses, further comprising an extraction cooling pipe extending between the compressor section and the turbine section, the extraction cooling pipe configured to convey bleed air from the compressor section to the turbine section for use by one or more turbine components, a contamination sensor disposed in the extraction cooling pipe and configured to provide data indicative of particulate contamination in the bleed air, the method further comprising implementing a control action associated with the washing system based on the data indicative of particulate contamination in the bleed air.

[0103] The method of one or more of these clauses, further comprising: determining, with the controller, when data indicative of particulate contamination in the bleed air exceeds a first contamination threshold and is below a second contamination threshold; and performing online washing of the gas turbine using a washing system in response to determining that the data indicative of particulate contamination in the bleed air exceeds the first contamination threshold and is below the second contamination threshold.

[0104] The method of one or more of these clauses, further comprising: determining, with the controller, when data indicative of particulate contamination in the bleed air exceeds a first contamination threshold and exceeds a second contamination threshold; shutting down the gas turbine; and performing an offline wash of the gas turbine using a washing system in response to determining that the data indicative of particulate contamination in the bleed air exceeds the first contamination threshold and exceeds the second contamination threshold.

[0105] The method according to one or more of these clauses, wherein the control action comprises generating a notification indicating that a maintenance action is required for the gas turbine.

[0106] The method of any one or more of these clauses, wherein the control action comprises adjusting a gas turbine wash schedule to minimize gas turbine downtime.

[0107] The method according to one or more of these clauses, wherein the control action comprises adjusting the concentration of detergent in the cleaning solution used by the cleaning system.

[0108] 1. A system comprising: a gas turbine including a compressor section, a combustion section, and a turbine section; a washing system fluidly coupled to the gas turbine; a particulate sensor disposed at at least one of an inlet to the compressor section or an outlet of the turbine section, the particulate sensor configured to provide data indicative of one or more particulate parameters; and a controller communicatively coupled to the washing system and the particulate sensor, the controller comprising a memory and at least one processor, the at least one processor configured to perform a plurality of operations, the plurality of operations comprising: monitoring, with the controller, data indicative of the one or more particulate parameters from the particulate sensor; determining, with the controller, when the data indicative of the one or more particulate parameters exceed a particulate threshold; and implementing a control action associated with the washing system in response to determining that the data indicative of the one or more particulate parameters exceed a particulate threshold.

[0109] The system described in one or more of these clauses, wherein the plurality of operations further comprises: using one or more models to determine at least one of a fouling rate, a deposition rate, and a deterioration rate associated with one or more components of the gas turbine based on data indicative of one or more particulate parameters; and implementing a control action associated with the washing system based on a magnitude of the at least one of the fouling rate, the deposition rate, and the deterioration rate.

[0110] The system described in one or more of these clauses further comprises a step of determining, by the controller, when one of the fouling rate, deposition rate, or deterioration rate exceeds a first rate threshold and falls below a second rate threshold, and a step of performing online washing of the gas turbine using the washing system in response to the step of determining that one of the fouling rate, deposition rate, or deterioration rate exceeds the first rate threshold and falls below the second rate threshold.

[0111] The system described in one or more of these clauses further comprises the steps of: determining, by a controller, when one of the fouling rate, deposition rate, or degradation rate exceeds a first rate threshold and a second rate threshold; shutting down the gas turbine; and performing offline washing of the gas turbine using a washing system in response to the step of determining that one of the fouling rate, deposition rate, or degradation rate exceeds the first rate threshold and a second rate threshold.

[0112] The system described in one or more of these clauses, further comprising an environmental sensor disposed outside the gas turbine, the environmental sensor communicatively coupled to the controller and configured to provide data indicative of the severe weather conditions, and the plurality of actions further comprising implementing a control action associated with the washing system based on the data indicative of the severe weather conditions.

[0113] The system described in one or more of these clauses, further comprising: determining, by the controller, when the data indicative of the severe weather conditions exceeds a first severe weather threshold and is below a second severe weather threshold; and performing online washing of the gas turbine with the washing system in response to determining that the data indicative of the severe weather conditions exceeds the first severe weather threshold and is below the second severe weather threshold.

[0114] The system of one or more of these clauses, further comprising: determining, by the controller, when the data indicative of the severe weather conditions exceeds a first severe weather threshold and exceeds a second severe weather threshold; shutting down the gas turbine; and performing an offline wash of the gas turbine with the wash system in response to determining that the data indicative of the severe weather conditions exceeds the first severe weather threshold and exceeds the second severe weather threshold. [Explanation of symbols]

[0115] 10 Gas turbines, turbine systems 12 Entrance 14 Compressor section 15 Entrance 16 Combustion section 18 Turbine section 19 Exit 20 Exhaust section 22 shaft 36 Inlet guide vane (IGV) 100 systems 102 Cleaning System 104 Washing Line 105 Intake 106 nozzle 107 Filtered Air 108 Detergent Selection System 110 Detergent Mixing System 112 Particulate Sensor 112A Particulate Sensor A 112B Particulate Sensor B 112C Particulate Sensor C 112D Particulate Sensor D 114 outer casing 116 rotor blades 118 Compressor flow path 120 Filter chamber 122 Vane filter 124 Fabric Filter 126 Electrostatic Components 128 Environmental Sensors 130 Extraction cooling pipe 132 Pollution Sensor 134 Fuel Supply System 136 Compressor sensor 137 Fuel supply source 138 Combustor Sensor 139 Fuel Line 140 Turbine Sensor 142 Filter chamber sensor 200 Controller 201 Sensor(s) 202 model(s) 204 Data Management System 206 processor(s) 208 Memory device(s), memory 210 Communication Module 212 Sensor Interface 214 Gas Turbine Operation Data 216 Environmental Data 218 Physics-based Engine Model 220 User Interface 222 Control Action 300 Real-time Deterioration / Soiling Adhesion Recommendation Algorithm Framework 500 model 502 Gas Turbine Operating Data 504 Sensor Data 506 Physics-Based Digital Models 508 Input 510 Output 512 Economic Impact 514 Cycle Time Effects 516 Particle Estimation Model 518 Maintenance Factor Estimation Model 520 Gas turbine engine control unit 522 one or more control actions 700 Logical Flowchart 710 Online Cleaning 712 Online Cleaning 800 ways

Claims

1. A method for timely addressing particulates in a gas turbine (10), the gas turbine (10) comprising a compressor section (14), a combustion section (16), and a turbine section (18), the method comprising: monitoring, with a controller (200), data indicative of one or more particulate parameters using a particulate sensor (112), the particulate sensor (112) being located at at least one of an inlet to the compressor section (14) or an outlet of the turbine section (18); determining, at the controller (200), when the data indicative of the one or more particulate parameters exceeds a particulate threshold; performing a control action associated with the cleaning system (102) in response to determining that the data indicative of the one or more particulate parameters exceeds the particulate threshold; A method for providing the above.

2. using one or more models to determine at least one of a fouling rate, a deposition rate, and a deterioration rate associated with one or more components of the gas turbine (10) based on data indicative of the one or more particulate parameters; performing the control action associated with the cleaning system (102) based on a magnitude of at least one of the soiling rate, the deposition rate, and the degradation rate; The method of claim 1 further comprising:

3. determining, with the controller (200), when one of the contamination rate, the deposition rate, or the degradation rate exceeds a first rate threshold and is below a second rate threshold; performing an online wash (710) of the gas turbine (10) using the washing system (102) in response to determining that one of the fouling rate, the deposition rate, or the deterioration rate exceeds the first rate threshold and is below the second rate threshold; The method of claim 2 further comprising:

4. determining, with the controller (200), when one of the contamination rate, the deposition rate, or the degradation rate exceeds the first rate threshold and exceeds the second rate threshold; shutting down the gas turbine (10); performing an offline wash (712) of the gas turbine (10) using the washing system (102) in response to determining that one of the fouling rate, the deposition rate, or the deterioration rate exceeds the first rate threshold and exceeds the second rate threshold; The method of claim 3 further comprising:

5. and further comprising an environmental sensor disposed external to the gas turbine, the environmental sensor communicatively coupled to the controller and configured to provide data indicative of severe weather conditions, the method comprising: implementing the control action associated with the washing system (102) based on data indicative of the severe weather conditions; The method of claim 1 further comprising:

6. determining, at the controller (200), when the data indicative of severe weather conditions exceeds a first severe weather threshold and is below a second severe weather threshold; performing an online wash (710) of the gas turbine (10) with the washing system (102) in response to determining that the data indicative of the severe weather conditions exceeds the first severe weather threshold and is below the second severe weather threshold; The method of claim 5 further comprising:

7. determining, at the controller (200), when the data indicative of severe weather conditions exceeds the first severe weather threshold and exceeds the second severe weather threshold; shutting down the gas turbine (10); performing an offline wash (712) of the gas turbine (10) with the washing system (102) in response to determining that the data indicative of severe weather conditions exceeds the first severe weather threshold and exceeds the second severe weather threshold; The method of claim 6 further comprising:

8. and a contamination sensor disposed in the extraction cooling pipe and configured to provide data indicative of particulate contamination in the bleed air; the method further comprising: an extraction cooling pipe extending between the compressor section and the turbine section, the extraction cooling pipe configured to convey bleed air from the compressor section to the turbine section for use by one or more turbine components; and a contamination sensor disposed in the extraction cooling pipe and configured to provide data indicative of particulate contamination in the bleed air; performing the control action associated with the scrubbing system (102) based on data indicative of the particulate contamination in the bleed air; The method of claim 1 further comprising:

9. determining, with the controller (200), when data indicative of particulate contamination in the bleed air exceeds a first contamination threshold and is below a second contamination threshold; performing online washing (710) of the gas turbine (10) using the washing system (102) in response to determining that the data indicative of particulate contamination in the bleed air exceeds the first contamination threshold and is below the second contamination threshold; The method of claim 8 further comprising:

10. determining, with the controller (200), when the data indicative of the particulate contamination in the bleed air exceeds the first contamination threshold and exceeds the second contamination threshold; shutting down the gas turbine (10); performing an offline wash (712) of the gas turbine (10) using the washing system (102) in response to determining that the data indicative of particulate contamination in the bleed air exceeds the first contamination threshold and exceeds the second contamination threshold; The method of claim 9 further comprising:

11. The method of claim 1 , wherein the control action comprises generating a notification indicating that a maintenance action is required for the gas turbine (10).

12. The method of claim 1 , wherein the control action comprises adjusting a wash schedule for the gas turbine to minimize downtime of the gas turbine.

13. The method of claim 1 , wherein the control action comprises adjusting a concentration of detergent in a cleaning solution used by the cleaning system (102).

14. a gas turbine (10) including a compressor section (14), a combustion section (16), and a turbine section (18); a washing system (102) fluidly coupled to the gas turbine (10); a particulate sensor (112) disposed at at least one of an inlet to the compressor section (14) or an outlet of the turbine section (18), the particulate sensor (112) configured to provide data indicative of one or more particulate parameters; a controller (200) communicatively coupled to the cleaning system (102) and the particulate sensor (112), the controller (200) comprising a memory (208) and at least one processor (206), the at least one processor (206) configured to perform a plurality of operations, the plurality of operations including: monitoring, with the controller (200), data indicative of the one or more particulate parameters from the particulate sensor (112); determining, at the controller (200), when the data indicative of the one or more particulate parameters exceeds a particulate threshold; implementing a control action associated with the cleaning system (102) in response to determining that the data indicative of the one or more particulate parameters exceeds the particulate threshold; The controller (200) comprises: A system that includes:

15. The plurality of operations: determining, using one or more models (202), at least one of a fouling rate, a deposition rate, and a deterioration rate associated with one or more components of the gas turbine (10) based on data indicative of the one or more particulate parameters; performing the control action associated with the cleaning system (102) based on a magnitude of at least one of the soiling rate, the deposition rate, and the degradation rate; The system of claim 14 further comprising:

16. determining, with the controller (200), when one of the contamination rate, the deposition rate, or the degradation rate exceeds a first rate threshold and is below a second rate threshold; performing an online wash (710) of the gas turbine (10) using the washing system (102) in response to determining that one of the fouling rate, the deposition rate, or the deterioration rate exceeds the first rate threshold and is below the second rate threshold; The system of claim 15 further comprising:

17. determining, with the controller (200), when one of the contamination rate, the deposition rate, or the degradation rate exceeds the first rate threshold and exceeds the second rate threshold; shutting down the gas turbine (10); performing an offline wash (712) of the gas turbine (10) using the washing system (102) in response to determining that one of the fouling rate, the deposition rate, or the deterioration rate exceeds the first rate threshold and exceeds the second rate threshold; The system of claim 16 further comprising:

18. an environmental sensor (128) disposed external to the gas turbine (10), the environmental sensor (128) communicatively coupled to the controller (200) and configured to provide data indicative of severe weather conditions, and wherein the plurality of operations comprises: The system of claim 14, further comprising: implementing the control action associated with the washing system (102) based on data indicative of the severe weather conditions.

19. determining, at the controller (200), when the data indicative of severe weather conditions exceeds a first severe weather threshold and is below a second severe weather threshold; performing an online wash (710) of the gas turbine (10) using the washing system (102) in response to determining that the data indicative of severe weather conditions exceeds the first severe weather threshold and is below the second severe weather threshold; 20. The system of claim 18, further comprising:

20. determining, at the controller (200), when the data indicative of severe weather conditions exceeds the first severe weather threshold and exceeds the second severe weather threshold; shutting down the gas turbine (10); performing an offline wash (712) of the gas turbine (10) using the washing system (102) in response to determining that the data indicative of severe weather conditions exceeds the first severe weather threshold and exceeds the second severe weather threshold; 20. The system of claim 19, further comprising:

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