Patent Application
20180196894
The present invention relates generally to a system and method for monitoring the operational conditions of a steam turbine and developing adaptive inspection intervals. More specifically, the present invention describes a system and method that develops customized steam turbine inspection/maintenance interval recommendations based upon one or more of steam quality assessment data acquired and calculated in real-time during actual plant operations of an individual steam turbine, relevant corrosion level diagnostics performed upon the steam turbine during a stand-still condition, and historical plant/service event digital database information to adapt a conventionally prescribed fleet inspection/maintenance interval of a particular steam turbine to specific plant operational conditions.
Steam turbines are widely used in industrial and commercial operations. As shown in
Steam turbines, like any other mechanical device, require periodic repairs and maintenance to ensure proper operation. As a general approach, previous experiences with the “fleet” of steam turbines, i.e., particularly comparable steam turbines of similar class or type, may be statistically analyzed to develop a “fleet model” that can be used to predict the anticipated wear and damage experienced by other steam turbines. Based on the fleet model, projections, repairs, inspections and maintenance can be scheduled at optimum intervals that minimize the risk of both unplanned shutdowns to effect repairs and also unnecessary shutdowns to perform unneeded preventive maintenance.
The actual performance of individual steam turbines, however, may vary from the fleet model. For example, individual steam turbines may have slight differences in configuration, manufacturing tolerances, and assembly that can result in different levels of wear and damage compared to the fleet model. In addition, the operational conditions, repair, and maintenance that individual steam turbines actually experience may differ from the fleet average. For example, steam turbines operated in adverse environments or with poor quality corrosive steam may require more frequent inspections, repairs and maintenance to address issues associated with corrosion, pitting, and emissions compared to the fleet model. Conversely, other steam turbines that are operated under optimal conditions and using good quality steam may experience fewer startups and/or shutdown cycles and, therefore, would require less frequent shutdowns to perform inspections and preventive maintenance associated with cyclical stresses as compared to the fleet model standard. Conventionally, however, a fixed inspection/maintenance interval for a particular steam turbine is developed according to a fleet model formula and is then prescribed for a plant regardless of the particular plant operational conditions for the turbine. For example, steam turbine inspection recommendations for a particular turbine fleet are typically derived from a fixed formula for total EOH (effective hours of operation) that is calculated using a factor based on number of turbine starts and logged hours of operating time. Consequently, there is little or no flexibility for an individual plant regarding scheduling of inspections and maintenance shutdown times that result in system unavailability. Actual real-time measured operational conditions of a steam turbine in situ at a particular plant are conventionally not obtained or considered in determining an inspection/maintenance interval for a steam turbine. Likewise, historical service events/records and turbine corrosion inspection findings at a particular plant are rarely if ever considered in determining the inspection/maintenance interval for a particular turbine. In particular, there is no consideration of operations at individual plants which may be using a higher quality steam and/or have low corrosion and which, therefore, require less maintenance and would benefit economically from an extended inspection interval. A similar consideration applies for steam turbines in plants operated with poor quality steam and/or under adverse conditions which may require more maintenance and should have inspections conducted at intervals shorter than the prescribed fixed-interval fleet formula.
Therefore, an improved system and method for monitoring the real-time operational conditions of a steam turbine and adapting or determining an inspection interval that is specific to individual plant operational conditions would be desirable.
A described system and method allows for a prescribed inspection interval for a steam turbine to be adapted based upon specific plant actual operational conditions and real-time operational data associated with individual steam turbines. This approach allows for extended inspection intervals for plants that use high quality steam and exhibit good operational behaviors. It also provides an ability to customize or modify a fleet prescribed interval/schedule for conducting inspections, maintenance and repairs based on individual plant/turbine operational conditions.
One non-limiting example embodiment of the present invention is a method for adapting a fleet prescribed inspection interval for a steam turbine based upon an operational profile developed from real-time monitored operational parameters. The method includes obtaining real-time measurements of one or more operational conditions/parameters of the steam turbine during its service in operation and calculating an operational profile quality value. The calculated operational profile value is then used to determine an adapted inspection interval or may be further evaluated along with obtained historical plant/service event information regarding the steam turbine and/or other obtained diagnostic information concerning the current condition of the steam turbine. For example, the method may include using a cold end corrosion diagnostic of the steam turbine for determining existing corrosion levels as other obtained diagnostic information.
Another non-limiting example embodiment of the present invention is a method for monitoring the operating environment and/or the operational conditions of a particular steam turbine in service at a particular plant and includes receiving or accessing data from a database containing plant/service event information relating to the steam turbine, measuring steam conductivity as an indicator of corrosive content of the steam and calculating a steam quality value, performing/obtaining stand-still corrosion diagnostics of the turbine (e.g., cold end corrosion diagnostic) and evaluating the combined information to develop an adapted/modified inspection interval and/or schedule for maintenance. The method includes calculating a steam quality value based upon steam conductivity (i.e., cation content) measured in-situ in real-time. The method may also include evaluating the steam quality value along with other operational parameters and/or obtained historical plant/service event information and/or obtained corrosion diagnostic information. The method may further include generating an output signal/information containing at least one of an operational quality valuation, an expected condition forecast for the steam turbine and an inspection/maintenance interval schedule.
A still further non-limiting example embodiment of the present invention is a system for monitoring the operating environment and/or the operational conditions of a steam turbine in service at a particular plant and generating inspection interval schedule information for the turbine. The system includes at least a processor, an I/O interface, one or more turbine steam conductivity sensors, and a memory element containing a database of at least historical plant/service event information for the turbine, wherein the I/O interface accepts one or more sensor signal indicative of steam conductivity obtained at one or more steam inlets of the steam turbine. It should be noted, however, that the invention disclosed herein is not limited solely to the use of steam conductivity as a measurable parameter indicative of the steam quality or “salt impact”. For example, steam conductivity could be replaced by ion chromatography using a sensor that measures Chloride and Sulfate concentrations in the ppb range. Using data based on Chloride and Sulfate concentrations, a steam conductivity may be calculated or, alternatively, the chloride/sulfate concentrations themselves may be used to determine a steam quality value. The processor being in communication with the memory element and performing a calculation in real-time indicative of the steam quality provided to the turbine, the calculated steam quality being used alone or in conjunction with obtained historical plant/service information regarding the steam turbine and/or one or more turbine operational condition factors to develop, adapt or modify an inspection/maintenance interval schedule for the steam turbine. An output information/signal generated by the processor may include at least one of an operational quality valuation, an expected condition forecast for the steam turbine and an inspection/maintenance interval schedule for display or printing via the I/O interface.
Those of ordinary skill in the art will better appreciate the features and aspects of the above example embodiments, and others, upon review of the description and specifications disclosed herein.
These and other features, aspects, and advantages of the present invention 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:
Reference will now be made in detail to non-limiting example embodiments of the invention which are illustrated in the accompanying drawings. Each example is provided by way of explanation of the invention, not limitation of the invention. It will be apparent to those skilled in the art that modifications and variations can be made in the present invention without departing from the scope or spirit thereof. For instance, features illustrated or described as part of one embodiment may be used on another embodiment to yield a still further embodiment. Thus, it is intended that the present invention covers such modifications and variations as come within the scope of the appended claims and their equivalents.
The system and method discussed herein may make reference to processors, servers, memories, databases, software applications, and/or other computer-based systems, as well as actions taken and information sent to and from such systems. One of ordinary skill in the art will recognize that the inherent flexibility of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among the components. For instance, computer-implemented processes discussed herein may be implemented using a single server or processor or multiple such elements working in combination. Databases and other memory/media elements and applications may be implemented on a single system or distributed across multiple systems. Distributed components may operate sequentially or in parallel. All such variations as will be understood by those of ordinary skill in the art are intended to come within the spirit and scope of the present subject matter.
When data is obtained or accessed between a first and second computer system, processing device, or component thereof, the actual data may travel between the systems directly or indirectly. For example, if a first computer accesses a file or data from a second computer, the access may involve one or more intermediary computers, proxies, or the like. The actual file or data may move between the computers, or one computer may provide a pointer or metafile that the second computer uses to access the actual data from a computer other than the first computer.
The various computer system(s) discussed herein are not limited to any particular hardware architecture or configuration. Embodiments of the methods and systems set forth herein may be implemented by one or more general-purpose or customized computing devices adapted in any suitable manner to provide desired functionality. The device(s) may be adapted to provide additional functionality, either complementary or unrelated to the present subject matter. For instance, one or more computing devices may be adapted to provide the described functionality by accessing software instructions rendered in a computer-readable form. When software is used, any suitable programming, scripting, or other type of language or combinations of languages may be used to implement the teachings contained herein. However, software need not be used exclusively, or at all. For example, as will be understood by those of ordinary skill in the art without required additional detailed discussion, some embodiments of the methods and systems set forth and disclosed herein may also be implemented by hard-wired logic or other circuitry, including, but not limited to application specific circuits. Of course, various combinations of computer-executed software and hard-wired logic or other circuitry may be suitable, as well.
It is to be understood by those of ordinary skill in the art that embodiments of the methods disclosed herein may be executed by one or more suitable computing devices that render the device(s) operative to implement such methods. As noted above, such devices may access one or more computer readable media that embody computer-readable instructions which, when executed by at least one computer, cause the at least one computer to implement one or more embodiments of the methods of the present subject matter. Any suitable computer-readable medium or media may be used to implement or practice the presently-disclosed subject matter, including, but not limited to, diskettes, drives, and other magnetic-based storage media, optical storage media, including disks (including CD-ROMS, DVD-ROMS, and variants thereof), flash, RAM, ROM, and other solid-state memory devices, and the like.
The example methods and system described herein allows for a prescribed inspection/maintenance interval for an individual steam turbine to be adapted based upon specific real-time measured/monitored plant operational conditions, such as ongoing steam quality, and/or other known operation effecting conditions and event data associated with the individual steam turbine. Adapting an inspection/maintenance interval of a steam turbine on an individual basis according to specific plant operational conditions results in numerous benefits for a plant operator/customer such as extending turbine availability, providing greater operational flexibility and reducing unnecessary shutdowns, thereby improving the economics of operation over the life of the steam turbine.
As an example, if analysis of the unit specific data for operation of a particular steam turbine at a specific plant indicates a low wear and low likelihood of corrosive damage under the ongoing operational conditions, the availability of that particular steam turbine may be increased by extending the fixed/static inspection intervals conventionally prescribed in accordance with a fleet model formula. Conversely, if analyzed unit specific data for a particular steam turbine at a specific plant indicates greater wear/corrosion or an increased potential for corrosive damage due to the operational conditions at the specific plant, the fixed/static interval prescribed by the fleet model formula may be decreased, resulting in planned outages occurring at shorter intervals. In either case, the ability to have adjustable inspection/maintenance intervals based upon specific plant operational conditions will result in enhanced operational flexibility and improves the costs of operation throughout the useful life of the steam turbine.
The processor 220 discussed herein is not limited to any particular hardware architecture or configuration. Instead, the processor 220 may comprise a general-purpose or customized computing device adapted to provide the described functionality by accessing memory media (e.g., blocks 36, 38, and/or 40), databases, and other hardware as directed by software instructions rendered in a computer-readable form or programmed circuitry. For example, the processor 220 may comprise a single server, a single micro-processor, hardwired logic, including, but not limited to, application-specific circuits, or multiple such elements working in combination.
In one example implementation, processor 220 is configured to retrieve and receive fleet model information signals and/or historical plant/service event signals 240 from database 210 to obtain or store relevant turbine information and data. For the purposes of the present discussion, the term “signals” refers to any electrical transmission of information. The fleet model and plant/service event information 240 may comprise, for example, data for comparable steam turbines projected by a fleet model and/or historical plant/service event information for a particular steam turbine at a particular plant. System 200 may also include a plant-based storage memory device 250 and processor 220 may contain internal data storage memory 230 for storage of turbine operational information, data and signals.
The database 210 discussed herein contains historical parameter information of the “fleet” of steam turbines, particularly comparable steam turbines of similar class or type, accumulated from available sources. The database 210 may include memory/media elements and applications implemented on a single system or distributed across multiple systems. If distributed components are used, they may operate sequentially or in parallel.
The historical parameter information contained in database 210 includes data reflecting operation, repairs, and/or maintenance of the comparable steam turbines. This historical parameter information may include data referred to as exposure data and damage data. Exposure data includes any information describing the operational history of a comparable steam turbine that can be statistically associated with predicting a failure mode or mechanism. For example, exposure data may include operating hours, number of start-up and shut-down cycles, steam temperatures, and number of unplanned trips. Damage data includes any hardware failure mechanisms that have occurred with a statistical significance. A failure mechanism includes any degradation in the physical or functional characteristics from the nominal values that results in a loss of output, loss of efficiency, or inability to operate the comparable steam turbine. Examples of known failure mechanisms include corrosion, creep, deformation, fatigue, foreign object damage, oxidation, plugging/contamination, rupture, and wear. These failure mechanisms may be collected or recorded as a result of enhanced boroscope inspections, onsite monitoring, operating logs, repair logs, maintenance logs, and the like.
The available sources of historical information include, for example, databases of operating experiences, operating records, part inspection records, and field inspection reports. Examples of the historical information included in these sources include, but are not limited to, enhanced boroscope inspection (EBI) reports, phased array ultrasonic inspections, electronic records, monitoring and diagnostics (M&D) data, records of outage events, operating hours, starts, and trips, and service shop or repair data. The collection of the historical information, such as exposure and damage data, is statistically analyzed and normalized to develop the fleet model, also known as a data accumulation model. The fleet model projects parameter information such as the growth of damage during future exposures using the collected historical information, and the fleet model and/or the projected parameter information may be communicated to the processor 220, for example, via the Internet or other wired or wireless communications network.
Processor 220 is also configured to receive signals from one or more turbine operational condition sensors 260 used in monitoring ongoing operating conditions and/or parameters of a steam turbine at a plant and to receive user-inputted command and control signals from a local terminal device 280 via I/O interface 290. Processor 220 may utilize internal digital data storage memory device 230 for storage of operational condition sensor signals and/or may utilize an external or local plant-based digital data storage device 250 for storage and retrieval of digital information and signals. Acquisition of data signals and communications to processor 220 are handled by known conventional processes via processor I/O interface 290.
In one example implementation, turbine operational condition sensors 260 are steam conductivity sensors positioned in the steam lines provided to different steam pressure sections of the turbine (e.g., the HP and IP turbine sections).
Referring again to
In addition to the monitoring of a best steam quality during operation of a particular steam turbine in service at a plant, other operational conditions at the same plant may also be monitored and used in developing the adapted inspection interval for the turbine such as, for example, the best practice preservation conditions occurring during turbine stand-still or lay-up times. Referring again to
Referring again to
C=(Lcc−0.055)*τ [μS*h/cm*a] EQU. 1
where Lcc is a value for the measured cation conductivity (μS/cm) over a period τ of operational hours per annum (h/a).
The above steam quality “C-value” is based upon a cation conductivity measurement, Lcc, of the steam provided to drive the turbine. Cation conductivity (μS/cm) (as well as degassed cation conductivity) is a standard measure for the sum of all dissociated substances (e.g., salts, acids, bases and some organic substances) in liquids and is an actual substitute for a value of the total dissolved solids/matter (TDS), particularly where the TDS is less than 1 mg/liter. Since the conductivity of “pure” water at 25° C. is approximately 0.055 μS/cm, this value for pure water conductivity must first be subtracted from the measured cation conductivity, Lcc. Every measurement of conductivity of the steam above this particular pure water conductivity value indicates that the steam carries some impurities that may cause degradation, ageing or corrosion of the turbine. Theoretically, a permissible maximum cation conductivity for the steam for a “continuous normal operation” of a steam turbine is Lcc=0.2 μS/cm (where “continuous normal operation” is defined as a base load operation of, for example, 7500 total hours per year for a single turbine at a plant). However, a best and safer practice is to set an Lcc level for the steam that is less than 60% of this theoretical maximum limit. Accordingly, applying this “best practice” approach would require having a measured Lcc of: 0.2 μS/cm*60%=0.12 μS/cm or less. In other words, a steam cation conductivity of 0.12 μS/cm or less would be a fairly good operational value of cation conductivity (Lcc) to maintain during a continuous normal operation of the turbine.
Using the “best practice” value of 0.12 μS/cm for cation conductivity in Equ. 1 above results in a steam quality “C-value” of C=900 μS*h/(cm*a) for a normal continuously run turbine (i.e., approx. 7500 total hours per year operation). It should be noted that if a plant is running a steam turbine for shorter than this example “base load” hours of operation, the above calculated C-value would need to be normalized accordingly. Specifically, the calculated C-value would need to be divided by 7500 hours/year and then multiplied by the actual operational hours for that particular year. For Example, for a plant running only 4560 hours/year, using C=900 μS*h/(cm*a), then C(normalized)=900/7500*4560=547.2 μS*h/(cm*a).
As indicated at block 405 of
Ultimately, as indicated at block 407, processor 220 utilizes an I/O device 280 to provide a displayed or printed output of the adapted inspection interval or an inspection schedule based on the adapted interval.
Environmental factors such temperature and humidity at a plant can have a significant impact on a steam turbine during extended times of shut-down and non-operation of the turbine. If appropriate preservation measures are not taken during such turbine lay-up or stand-still outage times, premature ageing, degradation and corrosion of the turbine parts can occur. Consequently, good preservation practices during such outage times should be taken and may also be used as a basis for extending the prescribed interval for inspections. One example system that can be used to monitor the best practice preservation measures taken for a given turbine during lay-up or stand-still times is a Cold-End Corrosion Diagnostic (CECD) system. Such a system uses one or more sensors placed in the “cold-end” low pressure steam (LP) section of a turbine to monitor temperature and humidity conditions over time during the lay-up or stand-still periods.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
