Patent Publication Number: US-2017357223-A1

Title: System and method to enhance turbine monitoring with environmental information

Description:
BACKGROUND 
     The subject matter disclosed herein relates to turbomachinery, and more particularly, to monitoring turbine performance. 
     In power generation systems, turbines, such as gas turbines or steam turbines, may convert fuel and air (e.g., an oxidant) into rotational energy. For example, a gas turbine may compress the air, via a compressor, and mix the compressed air with the fuel to form an air-fuel mixture. A combustor of the gas turbine may then combust the air-fuel mixture and use energy from the combustion process to rotate one or more turbine blades, thereby generating rotational energy. The rotational energy may then be converted into electricity, via a generator, to be provided to a power grid, a vehicle, or another load. 
     Various parts of the turbine, such as the compressor, the combustor, or the one or more turbine blades, may degrade or corrode over time while being exposed to the air fuel mixture or other environmental agents. For instance, the air entering the gas turbine may include dust that enters the compressor. The dust may cause degradation, corrosion, or other damage to the compressor, resulting in unplanned outages or other failures. 
     BRIEF DESCRIPTION 
     Certain embodiments commensurate in scope with the originally claimed disclosure are summarized below. These embodiments are not intended to limit the scope of the claimed disclosure, but rather these embodiments are intended only to provide a brief summary of possible forms of the disclosure. Indeed, embodiments may encompass a variety of forms that may be similar to or different from the embodiments set forth below. 
     In a first embodiment, a control system for a gas turbine includes a processor configured to access data indicative of environmental conditions of a location of the gas turbine, predict an occurrence of an event associated with the gas turbine based on the environmental conditions, wherein the event comprises a change in operation of the gas turbine due to the environmental conditions; and send a signal indicating the occurrence of the event to an electronic device. 
     In a second embodiment, a method includes accessing, via a processor, data that indicative of environmental conditions of a location of the gas turbine, predicting, via the processor, an occurrence of an event associated with the gas turbine based on the environmental conditions, wherein the event comprises a change in operation of the gas turbine due to the environmental conditions, and sending a signal indicating the occurrence of the event to an electronic device. 
     In a third embodiment, a non-transitory computer readable medium includes instructions configured to be executed by a processor of a monitoring system, wherein the instructions including instructions configured to cause the processor to access data indicative of environmental conditions of a location of the gas turbine, predict an occurrence of an event associated with the gas turbine based on the environmental conditions, wherein the event comprises a change in operation of the gas turbine due to the environmental conditions, and send a signal indicating the occurrence of the event to an electronic device. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein: 
         FIG. 1  is a block diagram of a gas turbine system having a monitoring system, in accordance with an embodiment; 
         FIG. 2  is a block diagram of an architecture of the monitoring system of  FIG. 1 , in accordance with an embodiment; 
         FIG. 3  is a flow diagram of a method performed by the monitoring system of  FIG. 1 , in accordance with an embodiment; 
         FIG. 4  is a flow diagram of a method to determine rules based on a predicted event of the gas turbine system of  FIG. 1 , in accordance with an embodiment; 
         FIG. 5  is a flow diagram of a method performed by the monitoring system of  FIG. 1  to generate a signal based on a comparison of the rules of  FIG. 4  with operating parameters of the gas turbine system of  FIG. 1 , in accordance with an embodiment; 
         FIG. 6  is a flow diagram of a method performed by the monitoring system to predict the event of  FIG. 4 , in accordance with an embodiment; 
         FIG. 7  is a graph of data received by the monitoring system to monitor degradation of a compressor of the gas turbine system of  FIG. 1 , in accordance with an embodiment; and 
         FIG. 8  is a graph of data received by the monitoring system that predicted the event, in accordance with an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     One or more specific embodiments of the present disclosure will be described below. In an effort to provide a concise description of these embodiments, all features of an actual implementation may not be described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers&#39; specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. 
     Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure. 
     When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. 
     Embodiments of the present disclosure are related to monitoring systems for turbomachinery, such as gas turbines, steam turbines, and/or compressors. For example, a gas turbine may include one or more compressors, a combustor, and one or more turbine blades. The one or more compressors may receive air adjacent to the compressor and compress the air to be mixed with a fuel to form an air-fuel mixture. The combustor may then combust the air-fuel mixture and use energy from the combustion process to rotate rotors of the one or more turbines. Further, the one or more turbines may be coupled to a shaft that rotates due to rotation of the one or more turbines. The rotational energy of the shaft may be converted (e.g., via a generator) into electrical energy to provide electricity to one or more loads. 
     Over time, various parts of the turbine, such as the one or more compressors, the combustor, and/or the turbine blades, may degrade, corrode, or otherwise become damaged, resulting in unplanned outages, reduced efficiency, or failures of the various parts. Degradation, corrosion, or other damage may be caused by a variety of factors, such as the air and fuel used. For example, environmental and/or atmospheric conditions, such as dust, pollen, sulfuric dioxide gas, sulfate aerosols, and/or sea salt aerosols, may impact operation of the various parts of turbomachinery. For instance, a rate of corrosion of a turbine compressor may depend on sulfuric dioxide levels in the air adjacent to the compressor that will be compressed. As another example, as air flows into the compressor, there may be different amounts of dust that can degrade performance of blades of the compressor. As the amount of dust increases, there may be an increased likelihood of compressor fouling. Moreover, due to the atmospheric conditions and/or environmental conditions across a fleet, turbomachinery may degrade, corrode, or otherwise become damaged much faster than expected and cause compressor fouling, bleed valve opening, and/or inlet filter ineffectiveness. To improve operation of the turbine, a monitoring system may be used to monitor the degradation, corrosion, or other damage that occurs to the turbine. 
     With the foregoing in mind, in certain embodiments, the monitoring system may use environmental data indicative of environmental conditions (e.g., atmospheric and/or environmental conditions) to predict damage to the compressor, combustor, turbine blades, or the like. For example, environmental data indicating atmospheric conditions related to weather may be received by the monitoring system. The monitoring system may then predict an occurrence of an event associated with the gas turbine based on the environmental conditions. The monitoring system may then generate a signal indicating the prediction and may send the signal to some computing device, a database, a communication network, or the like. 
     By way of introduction,  FIG. 1  shows a diagram of a gas turbine system  10  that includes a gas turbine  12  and a monitoring system  14 . The gas turbine  12  may receive air  16  or another oxidant, such as oxygen, oxygen-enriched air, or oxygen-reduced air, and a fuel  18 . The air  16  may be from outside the gas turbine  12 , such as air adjacent to the system  10 . That is, the air  16  may include characteristics that correspond to characteristics of surrounding atmospheric and/or environmental conditions. The air  16  may enter one or more compressors  20 . While one compressor  20  is shown in  FIG. 1  as an example, two, three, or more compressors may be used. For example, a three compressor system may include a low pressure compressor, an intermediate pressure compressor, and a high pressure compressor that are coupled to each other to further compress the air  16  and provide compressed air for the combustion process. 
     The compressed air and the fuel  18  may be mixed to create an air-fuel mixture to be combusted in a combustor  22 . The combusted air-fuel mixture may then apply a force to rotate a rotor of a turbine  24 . While one turbine  24  is shown in  FIG. 1  as an example, the gas turbine system  10  may include two, three, or more turbines  24 . The turbine  24  may be operatively coupled to a shaft  26  that rotates as the rotor of the turbine  24  rotates. The rotational energy of the shaft  26  may then be converted to electricity via a generator  28 , to provide electricity to one or more loads  30 . Although the gas turbine  12  is described in detail with respect to  FIG. 1 , it should be borne in mind that it is simply meant to be illustrative as an example. The monitoring system  14  may be used with steam turbines, pumps, or compressors, or any asset suitable for receiving air or fuel with the characteristics of atmospheric and/or environmental systems. 
     The gas turbine system  10  may also include an on-site monitoring (OSM) system  34  to monitor conditions of the gas turbine  12  at a location  36  by receiving signals  40 ,  42 , and  44  from sensors on the gas turbine  12  indicating operating parameters of the compressor  20 , combustor  22 , and turbine  24 , respectively. For instance, the operating parameters received from the signals  40 ,  42 , and  44  from may be referred to as on-site monitoring (OSM) data and may include ambient conditions, compressor parameters, fuel flow, power output, or the like. The location  36  may be a geographic position, a site, a building, or a structure of where the gas turbine  12  is located. As described in detail below, the OSM data from the OSM system  34  may be sent to the monitoring system  14  that operates on a cloud data server and that uses environmental data. 
     Certain parts of the gas turbine system  10  may undergo maintenance procedures to increase a lifespan or improve operation of the given parts. For example, to reduce degradation of the compressor  20 , the compressor  20  may undergo a water wash, such as an online water wash or an offline water wash. In an online water wash, the gas turbine system  12  may continue to provide power during the water wash. In an offline water wash, the gas turbine system  12  may be disconnected and components may be cleaned. For example, an operator may hand clean the compressor  20  and may reach the first stages while the compressor is online, but be unable to reach pieces that impact operation of the compressor  20  without taking the compressor  20  offline. As such, the offline water wash may be a more thorough cleaning than the online water wash. 
     The monitoring system  14  and/or the OSM system may send signals to an electronic device  46  that may be located at the location  36  or at another location. For example, the electronic device  46  may be a computer, laptop, smartphone, tablet, or the like. Further, the electronic device  46  may communicate with the monitoring system and/or the OSM system via a transceiver  48 , a wired connection (e.g., Ethernet), or routed via a router. For instance, an operator may use the electronic device  46  to see reports, alarms, advisory actions, or visualizations generated by the monitoring system  14  and/or the OSM system  34 . 
     As described in detail below, environmental data may be used to predict rates of degradation of the compressor  20  over time. The predictions may then be used to determine procedures to reduce degradation of parts of the gas turbine system  12 . For instance, predicted degradation of the gas turbine  12  may be used to determine times for scheduling water washes that reduce outages caused by degradation and increase the lifespan of the compressor. 
       FIG. 2  shows a block diagram of the various data received and/or sent by the monitoring system  14  on a cloud data server. The monitoring system  14  may receive data from the OSM system  34  described with respect to  FIG. 1 . The OSM systems  34  may be located at one or more locations  36  and may be associated with one or more assets  12 . Further, multiple OSM systems  34  may provide the monitoring system  14  with fleet data related to each location  36  and asset  12  as well as data related to the customer  60 . 
     The monitoring system  14  may be a cloud-based system that receives and/or sends signals to the gas turbine system  10  indicating one or more alerts related to environmental data. The monitoring system  14  may include a processor  64  and memory  66 . The OSM system  34 , the electronic device  46 , and/or the environmental system  68  may each include similar hardware (e.g., processor and memory) as described below with respect to the monitoring system  14 . Further, the processor  64  will be used throughout this disclosure, although as one of ordinary skill in the art will appreciate, multiple processors may be used by the monitoring system  14 , for example, across a cloud platform. The processor  64  may be operatively coupled to the memory  66  to execute instructions for carrying out the presently disclosed techniques. These instructions may be encoded in programs or code stored in a tangible non-transitory computer-readable medium, such as the memory  66  and/or other storage. The processor  64  may be a general purpose processor (e.g., processor of a desktop/laptop computer), system-on-chip (SoC) device, or application-specific integrated circuit, or some other processor configuration. The memory  66 , in the embodiment, includes a computer readable medium, such as, without limitation, a hard disk drive, a solid state drive, diskette, flash drive, a compact disc, a digital video disc, random access memory (RAM), and/or any suitable storage device that enables the processor  64  to store, retrieve, and/or execute instructions and/or data. The memory  66  may include one or more local and/or remote storage devices. 
     The processor  64  may obtain environmental data from a global environmental database (e.g., environmental system  68 ). The environmental system  68  may provide environmental data that corresponds to the one or more locations  36  of the gas turbine systems  10 . For example, if a gas turbine system  10  is located in a city, state, province, region, country, or the like, then the monitoring system  14  may receive environmental data for that city, state, province, region, or country. 
     As mentioned briefly above, the processor  64  may obtain environmental data that includes atmospheric conditions related to weather tracked globally. For example, the processor  64  may receive indications of levels of sulfur dioxide (SO 2 ), sulfate aerosols, sea salt aerosols, atmospheric particulate matter with a diameter less than 2.5 micrometers (PM 2.5 ), humidity, temperature, or the like. Further, the processor  64  may determine predictions from levels of the atmospheric conditions by constructing models based on prior effects of the atmospheric conditions. The processor  64  may then determine one or more rules for improving lifespan of parts of the gas turbine system  10 . 
     The monitoring system  14  may determine a prediction based on received data, such as the environmental data, OSM data, or both. As described in detail below, the monitoring system  14  may determine the prediction by developing one or more models and/or performing one or more algorithms based on atmospheric conditions and previously received OSM data. Further, the processor  64  may then apply one or more rules  72  to the OSM data and, if a rule  72  is violated, escalate the violated rule  72  by generating an alert  74  or advisory information  76 , such as recommendations for corrective actions. 
     The one or more rules  72  may be thresholds that are established from prior data, calculations, or the like. For example, the rules may capture OSM data over time and establish baseline reference values (R) for power output, heat rate, efficiency, or the like. For example, over weeks, months, or years, and/or based on design information of a gas turbine  12 , the processor  64  may establish that the gas turbine  12  has an efficiency with a baseline reference value of 41%. In an embodiment, the processor  64  establishes, via MATLAB®, for example, the rules from the OSM data by performing statistical analysis of the OSM data. For instance, the processor  64  may determine that operating beyond one standard deviation from the baseline reference indicates that it is desirable for an operator to perform an offline water wash. For example, if efficiency decreases below 1%, 5%, 10%, 15% of the baseline reference value, the rule may be violated. The processor  64  may establish rules  72  based on a difference between the reference value and measurements (P) (e.g., |R−P|). As another example, the processor  64  may establish rules based on a relative difference between the reference value and the measurements (e.g., |R−P|/R). As yet another example, with respect to compressor, the processor  64  may establish rules  72  based on a slope (e.g., |R−P|/time since last online or offline water wash). 
       FIG. 3  is a data flow diagram of a process  84  performed by the monitoring system  14  of the gas turbine system  10 . The process  84  begins by receiving static data  94 , such as asset configuration data (e.g., type of one or more parts of the turbine, location  36  of the turbine, and/or fleet data), environmental data from the environmental system  68 , or the like. For example, the static data  94  may include prior performance data of the gas turbine system  10 , international standard organization (ISO) conditions, design conditions, and/or certain accessories available for the gas turbine system  10 . Further, the static data  94  may include correction curves, periodic testing data, baseline reference data, rated conditions, fleet degradation curves, cycle deck configurations, or the like. The static data  94  may be located on a server on the environmental system  68  and/or the OSM system  34 . 
     The processor  64  may also receive dynamic data  96 , such as OSM data indicating performance and/or efficiency of the compressor  20 , the combustor  22 , and/or the turbine  24 , from a central dynamic database  98 . Dynamic data  96  may include ambient conditions (e.g., temperature, pressure, humidity), efficiency (e.g., power generated for a given amount of fuel and/or air), inlet system conditions (e.g., temperature, pressure, bleed), compressor parameters (e.g., pressure flow), fuel flow and power output, and/or steam injection conditions located on the OSM system  34 . As shown in  FIG. 2 , in one embodiment, the central database of the OSM system  34  may be stored separately from the monitoring system  14 . 
     The processor  64  may aggregate the received static data  94  and dynamic data  96  as input data  100 . The monitoring system platform  102  may then, via the processor  64 , obtain the aggregated input data  100  and evaluate the gas turbine system  10  by determining a prediction based on the input data  100  and/or by evaluating performance degradation of the gas turbine system  10 , as described with respect to  FIG. 4  below. The processor  64  may then determine one or more rules  103  based on the prediction and compare the dynamic data  96  to the one or more rules. For instance, using a MATLAB® execution system  104  that identifies correlations between types of the aggregated data, the processor  64  may determine whether any rules have been violated. For example, the processor  64  may determine, via the MATLAB® rule execution system  104 , that dust in the atmosphere is correlated to an increased likelihood of compressor fouling. As such, the processor  64  may establish thresholds related to times between water washes based on dust level in the atmosphere during prior events of compressor fouling. 
     The processor  64  may obtain a trigger  106 , or dynamic data  96  that exceeds the thresholds of the one or more rules  103 . The triggers  106  may include environmental input data, degradation levels since the most recent water wash, and/or degradation levels with respect to a baseline value established from models and/or algorithms. Additional and/or alternatively, the triggers  106  may be received from the Matlab rule execution system  104 . The processor  64  may utilize the triggers  106  to determine the rules  103  to escalate  110  when rules  103  are violated. For instance, the processor  64  may generate an alarm  112 , provide advisory information  114 , or otherwise inform an operator of the violated rule  103 . 
     The triggers  106  (e.g., operating parameters of the gas turbine  12  that violate the rules  103 ) and/or the rules  103  may be stored as dynamic data  96  on the OSM system  34 . Alternatively and/or additionally, the rules  103  may be provided as a visualization  116  to the operator. For example, the processor  64  may generate reports related to fleet comparisons, event statistics, input trends, output trends, and/or environmental factors. The visualization  116  may include the triggers  106 , the rules  103 , and the dynamic data  96  used in the comparisons, and the visualization  116  may be stored with the dynamic data  96 . 
     In certain embodiments, the processor  64  may send a signal to the OSM system  34  to control operation of the gas turbine system  10  based on the predicted event. For example, the processor  64  may send a signal to shut down the gas turbine, to adjust fuel flow, to adjust the air/fuel ratio, or the like. That is, the processor  64  may control the gas turbine system  10  based on the alarm  112 . In some embodiments, the processor  64  may send the alarm  112  to the electronic device  46  cause the electronic device to activate and display an application that provides the alarm  112 , the advisory information  114 , or any combination thereof, to an operator to control operation of the gas turbine system  10 . 
       FIG. 4  is a data flow diagram showing a process  130  performed by the processor  64  of the monitoring system platform  102  to predict an event based on the dynamic data  96  and/or the static data  94 . An event can be any hardware failure (e.g., compressor blade crack, material loss, rotor crack, turbine blade crack or material loss), compressor fouling, inlet filter clogging, or any efficiency degradation of a component (compressor, turbine, or combustor). The event may be a forced outage or maintenance departure due to an anomaly in the system operation. For example, events may include performance of operating parameters of the gas turbine system  12  exceeding or falling below thresholds, such as degradation levels and/or corrosion levels exceeding a certain threshold since the last water wash, percent lower explosive limit (% LEL) of alcohol in the compressor falling below a threshold, or a calculated percent degradation and corrosion falling below a threshold. As another example, events may include compressor fouling, power outages, faults, compressor blade cracks, or the like. 
     The processor  64  may receive historical operational/monitoring data at block  132 , such as OSM data prior to the occurrence of an event. Further, the processor  64  may receive fleet-wide data at block  134  related to operation of multiple assets across multiple sites. The fleet-wide data may include OSM data from other locations  36 . As an example, fleetwide data may include average efficiency levels of a set of engines at various times. At block  136 , the processor  64  may receive event physics data that is data used in calculations based on the physics of the gas turbine system  10 . For instance, event physics data may include the thermodynamic modeling variables (e.g., turbine inlet pressure, stage one nozzle throat area, etc.) described below with respect to  FIG. 6 . The processor  64  may receive corrosion physics data, at block  138 , which is data related to calculating corrosion of the compressor  20 . For example, corrosion physics data may include a number of fired hours that the compressor has operated and that is used to calculate degradation. At block  140 , the processor  64  may receive the environmental data, such as the atmospheric conditions, as described above. 
     At block  142 , the processor  64  may receive asset maintenance data of actions taken by an operator to maintain operation of the gas turbine system  10 . Asset maintenance data may include data related to an operator performing a cleaning, water wash, testing, or the like. At block  144 , the processor  64  may receive customer configuration data of how the customer  60  associated with the asset has configured the gas turbine system  12  for operation. For example, the customer configuration data may include types of combustors or turbines used, a frequency in which the turbine is operating, types of fuel  18  used by the gas turbine system  10 , or the like. Further, the processor  64  may receive asset event data at block  146  of various prior events that are related to the gas turbine system  10 . For example, asset event data may include data related to degradation levels when compressor fouling has occurred previously, efficiency of the gas turbine system  10  prior when the last outage occurred, or the like. 
     As described in detail with respect to  FIG. 6 , the processor  64  may then predict the event, at block  148 , based on the historical data, fleet wide data, event physics data, corrosion physics data, environmental data, asset data, customer data, and/or asset data and data of the asset. For example, the processor  64  may predict when the next compressor fouling will occur based on the current degradation levels and environmental data that corresponds to rates that the current degradation levels may increase. As another example, the processor  64  may predict lube oil leakage of the gas turbine  12  based on when maintenance of compressor  20  was last performed. As yet another example, the processor  64  may predict when water quality or fuel quality entering the combustor  22  will fall below a threshold level based on environmental data that impacts the water and/or fuel system at the location  36 . At block  150 , the processor  64  may then determine one or more rules based on the prediction. 
       FIG. 5  illustrates a flow diagram of a method  158  performed by the processor  64 . At block  160 , the processor  64  may receive signals indicating operating parameters of the gas turbine system  10  from the OSM system  34  in real-time or otherwise received at an interval (e.g., from the last second, minute, five minutes, ten minutes, hour, etc.). The received operating parameters include ambient temperature, humidity, pressure, power output, fuel temperature, fuel flow, power output, turbine shaft speed, compressor inlet and exhaust pressure drop, or the like. At block  162 , the processor  64  may compare the received operating parameters to the one or more rules  150 , determined as described above, to determine whether any rules were violated. For example, the comparisons may include whether differences, relative differences, or slopes between reference values (R) and measured values (P) exceed a threshold. 
     At block  164 , the processor  64  may generate a signal indicating various results, such as violations of the rules. As another example, the prediction results may include ranked predictions based on a likelihood of the prediction, an urgency of the predictions, or both to enable an operator to prioritize maintenance to the gas turbine system  10 . Alternatively and/or additionally, the results may include one or more alerts  112  of events that are likely to occur, such as a message to an on-site operator, a message sent to the OSM system  34 , or the like. 
     Further, the processor  64  may send one or more advisory information  114  that indicate paths an operator may take to improve performance of the gas turbine system  10 . The advisory information  114  may include times for performing offline compressor water washes, maintenance actions, sensor calibrations, maintenance schedules, hardware upgrades, design improvements, or the like. For example, the advisory information  114  may indicate water wash ineffectiveness, water quality, fuel quality, air quality, dust levels, lube oil leakage, secondary flow leakage, or compressor fouling. If diagnostics show that the continuously monitored degradation rate is attributed to the lack of compressor washes, filter cleanings, or open bleed valves, the processor  64  may advise maintenance earlier than originally planned to ensure that the gas turbine system  10  operates efficiently. That is, the advisory information  114  may enable an operator to improve lifespan of the compressor  20  by providing data related to when and what type of water wash is desirable. The prediction results may further include one or more visualizations, such as charts, graphs, or lists of performance of efficiency of the engine over time as compared to operation of the combustor  20 . 
     The processor  64  may send a signal indicating the event, the violation of the rule, the advisory action, reports, visualizations, or the like. For example, the processor  64  may communicate the occurrence of the event to the electronic device  46  via email, phone, or web services. The site engineers may then access the emails, phone, or web services to inspect the event, the rules, the advisory actions, reports, visualizations, or the like. For example, an indication of predicted compressor fouling may be sent to the electronic device  46  for the operator to perform an offline water wash. 
       FIG. 6  is a block diagram of a process performed by the processor  64  to predict the event of block  148  of  FIG. 4 . At block  174 , the processor  64  may first determine one or more performance characteristics of the gas turbine via an algorithm and/or model. For example, using a thermodynamic modeling approach, the processor  64  may calculate turbine performance, via thermodynamic cycle matching, from a set of measured parameters, such as a compressor discharge temperature and pressure, exhaust temperature, fuel flow, and power output. That is, thermodynamic cycle matching may be used to determine non-measured performance parameters, such as compressor and turbine efficiencies, combustor exit temperature, and turbine firing temperature, for the gas turbine operated at specified conditions. For instance, the thermodynamic modeling approach may calculate turbine mass flow rate using the following equation: 
         f   gt   =g   air   ×P   in   ×A   S1N ×α f /√{square root over ( T   in )}  eq. (1)
 
     where f gt  is turbine mass flow rate, g air  is a flow calculation function, P in  is turbine inlet pressure, A S1N  is a stage one nozzle throat area, and α f  is a stage one nozzle flow coefficient, and T in  is the turbine total inlet temp. 
     The compressor mass flow rate (f comp ) follows the law of conservation of mass using the following equation: 
         f   comp   =f   gt   +f   ext   −f   fuel    eq. (2)
 
     where f ext  is extraction flow and f fuel  is fuel flow. The compressor and turbine power values may then be calculated via the thermodynamic heat balance with the law of conservation of mass. The gas turbine model is then used to project performance of the gas turbine system  10  under other operating conditions, such as under forecasted weather predictions at the location  36 . Further, the one or more performance characteristics of the gas turbine system  10  may then be calculated and corrected, via the thermodynamic cycle matching method, at baseload and ISO conditions. 
     The model may be integrated into the monitoring system  14  to track in real-time a trend of asset performance in terms of performance indicators (output, heat rate, flow, and efficiency). Corrective action will be recommended to the customer once an anomaly or significant degradation is detected, in order to improve the performance of the system going forward. The model may be used to troubleshoot the performance issues that the customer is concerned about, such as inlet filter clogging, bleed valve open, or any operation issue. Additionally and/or alternatively, the model may be used to conduct performance scenarios analysis to help upgrade the hardware parts so the system  10  can be operated at optimal performance, for instance, to upgrade a hot gas path, compressor  20 , or combustor  22 . 
     At block  176 , the processor  64  may determine degradation, corrosion, or other damage values of the gas turbine system  10  based on the performance characteristics of block  174 . Further, the degradation of the compressor  20  may be calculated with respect to the last offline water wash based on the compressor mass flow rate, as described above. The degradation may be based on generator watts, power factor, ambient conditions, fired hours, fuel system, compressor discharge pressure, discharge temperature, turbine pressure, exhaust temperature, or the like. 
     At block  178 , the processor  64  may then determine operating factors based on the performance characteristics, the degradation values, or other inputs (e.g., fleet-wide data, event physics, corrosion physics, environmental data, asset management configuration data, customer data, or asset event data). The operating factors may be factors that may be helpful in assessing whether the gas turbine system  10  performance is operating as desired. For example, compressor degradation may have different correlation coefficients for different performance characteristics. That is, degradation of the compressor may be found to have a stronger correlation to SO 2  than surface pressure. As such, degradation may be determined by weighing the performance characteristics with a greater correlation coefficient, SO 2 , more heavily than the performance characteristics with a lower correlation coefficient, surface pressure. 
     The processor  64  may select operating factors that are relatively independent of one another, such as time averaged surface pressure, humidity, temperature, dust, sea salt, sulfur dioxide, gas fuel flow, inlet air flow, and generator frequency. For example, turbine HP shaft speed falling within the range of 95% to 105% may be used to determine whether the unit is operating within desired operation and may therefore be an operating factor, and time averaged surface pressure may be selected as another operating factor because it is relatively independent of the shaft speed. Identifying factors may further include combining the identified factors using principal component analysis to weight the factors based on variance between the values of the factors. 
     At block  180 , the processor  64  may then generate a predictive model based on the performance data, OSM data, degradation data, environmental data, and/or critical factors by using the acquired data with known mathematical models, such as a physics-based model, an artificial neural network (ANN), deep learning, random forest, logistic regression, linear predictive modeling, or the like. Further, the model may be constructed as a function (e.g., linear) of the operating factors described with respect to block  178 . In some embodiments, the processor  64  may generate the predictive model by determining when current operating parameters will correspond to historical operating parameters associated with the similar events. For example, the processor  64  may establish a relationship between the operating parameters and prior events (e.g., log based, linear, quadratic, etc.). For instance, degradation of the compressor may historically follow a log-based relationship with respect to fired hours. Further, for environmental data, the processor  64  may utilize a general log-linear (GLL) relationship model with a Weibull distribution that reduces a number of variables of the acquired data. 
     At block  182 , the processor  64  may then predict a rate of damage based on the model. That is, the predictive model may be used to determine the rate (e.g., slope) of damage due to corrosion, a rate of power degradation, a rate of deterioration, a rate of change of a likelihood of corrosion causing unplanned outages, a rate of compressor fouling, or the like. For example, over time, blades of the compressor may have an increased tendency to crack when the compressor has a certain level of deterioration. The processor  64  may use historical data of other cracks in other compressors to generate the predictive model and determine a slope that the deterioration is likely to occur by fitting a curve to the predicted values of the predictive model at various points in time. 
     At block  184 , the processor  64  may then compare the predicted rate of damage with the dynamic data to determine performance degradation of the compressor  20 . For example, the dynamic data may include historical data, near real-time data, or forecasts (e.g., weather forecasts) of atmospheric and/or environmental data on airborne particulates, trace gas, unit configuration, event history, and/or operational parameters. The dynamic data may be included in the model to determine damage rates. For example, the processor  64  may determine, via the model and the dynamic data, current or future operational damage rates, probability and severity of fouling, probability and severity of corrosion, and/or unplanned outages. Further, the processor  64  may use current operating parameters as points. As such, in some embodiments, the processor  64  may then predict events based on the current operating parameters of the gas turbine system  10  (e.g., current operating point), the rate of damage, and the relationship. For example, the predictive model may be based on historical performance data, the rate of damage, and the dynamic data predict a degradation level at a given point in time. The processor  64  may then compare the degradation level at the given point of time to the rules as described in block  150  above and determine if any advisory information  114  is desired. 
       FIG. 7  shows a graph  200  of performance losses  202  with respect to time  204  of the compressor  20  as an example of the process performed in block  148 . As shown in the graph  200 , a line  206  represents non-recoverable performance losses of the turbine where the performance of the compressor  20  is permanently reduced. Further, a line  208  represents performance degradation that is recoverable between inspections at points  210  of the compressor  20 . That is, at inspections, performance losses of the compressor  20  are minimized to the non-recoverable performance losses. Line  212  represents degradation between offline water washes at points  214 , and line  216  represents degradation between online water washes at points  218 . As shown in  FIG. 8 , degradation generally increases over time. As described above, the processor  64  may determine a rate of damage, such as the rates of degradation shown in  FIG. 7 . For example, depending on dust levels, the compressor may have more rapid degradation as shown by line  220 . 
       FIG. 8  is a zoomed view  222  of the degradation shown by line  220  of the graph  220  of  FIG. 7  taken along lines A-A′. At a time  224  having a currently monitored status  226 , the processor  64  performs the process described with respect to  FIG. 6  and determines degradation levels based on performance characteristics of the gas turbine system  10 . The processor  64  then generates a predictive model  228  (the values after the currently monitored status  226  to project the degradation levels in the future (e.g., upcoming hours). The processor  64  may determine the rate of degradation  230  based on the predicted model  228 . The processor  64  may then predict an event  232  (e.g., in the future upcoming hours) based on the rate of degradation  230  and/or the predictive model  228 . For example, the event  232  may be a predicted power outage or a point where performance loss exceeds a threshold  233  above a desired reference  234 . 
     The processor  64  may generate a signal indicating that a water wash may be used to improve operation of the gas turbine  12 . Depending on the severity, the processor  64  may send a signal indicating that the water wash be an online water wash where the compressor  20  is cleaned while the gas turbine  12  is still in operation, an offline water wash where the compressor is cleaned while the gas turbine  12  is offline, or an inspection where losses are minimized to non-recoverable performance losses. To avoid a potential power outage at time  236 , the operator may perform an offline water wash earlier than originally schedule. In some embodiments, the processor  64  may send a signal to the OSM system  34  indicating a predicted fouling of the compressor based on previous fouling of one or more other compressors at one or more other locations. For example, the processor  64  may send a signal indicating an alert having advisory information to perform an offline water wash when components within the compressor likely have degradation based on the one or more parameters. Further, at time  224 , the water wash may reduce performance losses by a certain margin  240 . That is, the processor  64  may send a signal for an operator to perform a water wash when operating at the currently monitored status  226  so that the compressor operates at an operating point  242  where performance losses are reduced. By reducing performance losses based on the predicted event  232  and the rate of degradation, the processor  64  enables the compressor  20  to avoid operating at an operating point  232  before there is an increased likelihood of fouling (e.g., at the predicted event  232 ). In an embodiment, the processor  64  enables the compressor  20  to operate at an operating point  246  having a margin  244  below where the compressor  20  is predicted to foul at the predicted event  232 , thereby decreasing a likelihood of an outage. 
     Technical effects of the present disclosure include improved operation of a turbine and/or generating signals of alerts based on turbine performance. The system may include a processor that receives environmental data indicating atmospheric conditions of a location. The processor may predict one or more events based on the atmospheric conditions and output signals indicating an alert or advisory actions. The processor may send the output signals to an on-site monitoring system so that the alerts or advisory actions may be used to control operation of the turbine. By monitoring the gas turbine system in real time and notifying the operator, the processor may provide a post-solution activity in alerting the operator and/or controlling operation of the turbine based on predicted events and/or alerts. 
     This written description uses examples to disclose the embodiments, including the best mode, and also to enable any person skilled in the art to practice the embodiments, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the disclosure is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.