U.S. patent application number 14/382049 was filed with the patent office on 2015-01-29 for method and system for real time gas turbine performance advisory.
This patent application is currently assigned to Nuovo Pignone Srl. The applicant listed for this patent is Nuovo Pignone Srl. Invention is credited to Osama Naim Ashour, Ever Avriel Fadlun, Abdurrahman Abdalah Khalidi, Marco Pieri, Arul Saravanapriyan.
Application Number | 20150027212 14/382049 |
Document ID | / |
Family ID | 46051732 |
Filed Date | 2015-01-29 |
United States Patent
Application |
20150027212 |
Kind Code |
A1 |
Fadlun; Ever Avriel ; et
al. |
January 29, 2015 |
METHOD AND SYSTEM FOR REAL TIME GAS TURBINE PERFORMANCE
ADVISORY
Abstract
A system and method for monitoring and diagnosing anomalies in
an output of a gas turbine, the method including storing a
plurality rule sets specific to a performance of the gas turbine.
The method further including receiving real-time and historical
data inputs relating to parameters affecting the performance of the
gas turbine, periodically determining current values of the
parameters, comparing the initial values to respective ones of the
current values, determining a degradation over time of the at least
one of the performance of the compressor, the power output, the
heat rate, and the fuel consumption based on the comparison,
recommending to an operator of the gas turbine a set of corrective
actions to correct the degradation.
Inventors: |
Fadlun; Ever Avriel;
(Florence, IT) ; Khalidi; Abdurrahman Abdalah;
(Doha, QA) ; Pieri; Marco; (Florence, IT) ;
Saravanapriyan; Arul; (Doha, QA) ; Ashour; Osama
Naim; (Doha, QA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Nuovo Pignone Srl |
Florence |
|
IT |
|
|
Assignee: |
Nuovo Pignone Srl
Florence
IT
|
Family ID: |
46051732 |
Appl. No.: |
14/382049 |
Filed: |
March 1, 2013 |
PCT Filed: |
March 1, 2013 |
PCT NO: |
PCT/EP2013/054154 |
371 Date: |
August 29, 2014 |
Current U.S.
Class: |
73/112.05 |
Current CPC
Class: |
G05B 11/06 20130101;
F02C 7/00 20130101; H04L 67/10 20130101; G01M 15/14 20130101; G05B
23/0216 20130101; F05D 2260/80 20130101; G05B 23/0245 20130101;
G05B 23/0283 20130101; F04B 51/00 20130101; F01D 21/003 20130101;
G01L 3/10 20130101; G05B 23/0218 20130101; G01K 13/00 20130101 |
Class at
Publication: |
73/112.05 |
International
Class: |
G01M 15/14 20060101
G01M015/14; G01L 3/10 20060101 G01L003/10; F04B 51/00 20060101
F04B051/00 |
Foreign Application Data
Date |
Code |
Application Number |
Mar 1, 2012 |
IT |
CO2012A000008 |
Claims
1. A computer-implemented method for monitoring and diagnosing
anomalies in an output of a gas turbine, the method implemented
using a computer device coupled to a user interface and a memory
device, the method comprising: storing a plurality rule sets in a
memory device, the rule sets relative to the output of the gas
turbine, the rule sets comprising at least one rule expressed as a
relational expression of a real-time data output relative to a
real-time data input, the relational expression being specific to
at least one of a performance of a compressor of the gas turbine, a
power output of the gas turbine, a heat rate of the gas turbine,
and a fuel consumption of the gas turbine; receiving real-time and
historical data inputs from a condition monitoring system
associated with the gas turbine, the data inputs relating to
parameters affecting at least one of the performance of the
compressor, the power output, the heat rate, and the fuel
consumption; determining initial values of at least one of the
performance of the compressor, the power output, the heat rate, and
the fuel consumption; periodically determining current values of at
least one of the performance of the compressor, the power output,
the heat rate, and the fuel consumption; comparing the determined
initial values to respective ones of the current values;
determining a degradation over time of the at least one of the
performance of the compressor, the power output, the heat rate, and
the fuel consumption based on the comparison; and recommending to
an operator of the gas turbine a set of corrective actions to
correct the degradation.
2. The method of claim 1, further comprising determining a axial
compressor efficiency using a temperature dependent specific heat
ratio.
3. The method of claim 1, further comprising correcting the
determined efficiency for an actual speed of the axial compressor
and an actual opening of an inlet guide vane.
4. The method of claim 1, wherein determining values of the power
output comprises at least one of receiving a power output signal
from a torquemeter and determining the power output using an energy
balance algorithm to generate a calculate power output signal.
5. The method of claim 1, wherein determining values of the fuel
consumption comprises determining values of the fuel consumption at
least one of full load and partial load.
6. A gas turbine monitoring and diagnostic system for a gas turbine
comprising an axial compressor and a low pressure turbine in flow
communication, said gas turbine monitoring and diagnostic system
comprising: a gas turbine performance rule set, the rule set
comprising a relational expression of a real-time data output
relative to at least one of a performance of a compressor of the
gas turbine, a power output of the gas turbine, a heat rate of the
gas turbine, and a fuel consumption of the gas turbine.
7. The system of claim 6, wherein said rule set is configured to:
receive real-time and historical data inputs from a condition
monitoring system associated with the gas turbine, the data inputs
relating to parameters affecting at least one of the performance of
the compressor, the power output, the heat rate, and the fuel
consumption; and determine initial values of at least one of the
performance of the compressor, the power output, the heat rate, and
the fuel consumption; periodically determine current values of at
least one of the performance of the compressor, the power output,
the heat rate, and the fuel consumption; compare the determined
initial values to respective ones of the current values; determine
a degradation over time of the at least one of the performance of
the compressor, the power output, the heat rate, and the fuel
consumption based on the comparison; recommend to an operator of
the gas turbine a set of corrective actions to correct the
degradation.
8. The system of claim 6, wherein the rule set is configured to
verify an operability of sensors providing the real-time data
inputs using at least one of the historical data inputs and a
thermodynamic simulation algorithm.
9. The system of claim 6, wherein the rule set is configured to
determine an axial compressor efficiency using a temperature
dependent specific heat ratio.
10. The system of claim 6, wherein the rule set is configured to
determine a temperature at which the ratio is evaluated using an
empirical correlation.
Description
FIELD OF THE INVENTION
[0001] This description relates to generally to
mechanical/electrical equipment operations, monitoring and
diagnostics, and more specifically, to systems and methods for
automatically advising operators of anomalous behavior of
machinery.
BACKGROUND OF THE INVENTION
[0002] Increasing the efficiency of gas turbines and optimizing
their performance is a top priority for oil and gas manufacturers
and customers. All gas turbines require routine maintenance at
various intervals, for example, ranging from approximately every
6000 hours to approximately every two years. Many factors
contribute to the performance degradation that necessitates such
maintenance, such as those related to the axial compressor and hot
gas path components. Axial compressor-related degradation may occur
due to blade fouling and corrosion, and inlet filter pressure drop
due to clogging. Foreign deposits that pass through inlet filters
can accumulate on the compressor blades. This results in a drop of
axial compressor efficiency and pressure ratio, which, in turn,
results in an output performance drop such as a reduction in output
power and thermal efficiency. This drop in output performance may
be able to reach 5% within a month of operation. With the exception
of blade corrosion and fatigue, most axial compressor-related
problems can be reversed with routine maintenance. On-line and
off-line water washing are used periodically to restore the machine
operating condition. Similarly, replacing the inlet filter can
reduce performance degradation due to filter clogging. Therefore,
continuous monitoring of the machine to detect early signs of
degradation facilitates prolonged shut down periods. Continuous
monitoring also allows for performance optimization by adapting
some of the process parameters, environmental conditions, or
maintenance schedules.
[0003] Traditional performance monitoring systems use generic
formulas and thermodynamic equations to calculate performance
metrics. The specific design and control strategy for the monitored
gas turbine are not accounted for. Expected performance is thus
only theoretical and does not correspond to actual monitored
machines. Many assumptions are used that result in significant
errors. Hence, such systems cannot detect early signs of
degradation. For example, a change in compressor efficiency of only
1-2% may indicate the need of a water wash. Correction factors are
not used accurately to account for different control strategies. In
addition, output performance rules are valid for full load
conditions only. It is well known, however, that machines are
routinely operated under part load conditions. Accordingly, such
rules are not typically valid for all load ranges. Also, no link is
provided between output and input parameters. Each component is
monitored separately. Thus, troubleshooting is not facilitated.
Another major drawback of known monitoring systems is that their
rules depend on data from sensors that do not exist or are
malfunctioning, resulting in rules that are inaccurate or
obsolete.
SUMMARY OF THE INVENTION
[0004] In one embodiment, a computer-implemented method for
monitoring and diagnosing anomalies in an output of a gas turbine
wherein the method is implemented using a computer device coupled
to a user interface and a memory device, and wherein the method
includes storing a plurality rule sets in the memory device, the
rule sets relative to the output of the gas turbine, the rule sets
including at least one rule expressed as a relational expression of
a real-time data output relative to a real-time data input, the
relational expression being specific to at least one of a
performance of a compressor of the gas turbine, a power output of
the gas turbine, a heat rate of the gas turbine, and a fuel
consumption of the gas turbine. The method further includes
receiving real-time and historical data inputs from a condition
monitoring system associated with the gas turbine, the data inputs
relating to parameters affecting at least one of the performance of
the compressor, the power output, the heat rate, and the fuel
consumption, determining initial values of at least one of the
performance of the compressor, the power output, the heat rate, and
the fuel consumption, periodically determining current values of at
least one of the performance of the compressor, the power output,
the heat rate, and the fuel consumption, comparing the determined
initial values to respective ones of the current values,
determining a degradation over time of the at least one of the
performance of the compressor, the power output, the heat rate, and
the fuel consumption based on the comparison, and recommending to
an operator of the gas turbine a set of corrective actions to
correct the degradation.
[0005] In another embodiment, a gas turbine monitoring and
diagnostic system for a gas turbine that includes an axial
compressor and a low pressure turbine in flow communication
includes a gas turbine performance rule set, the rule set including
a relational expression of a real-time data output relative to at
least one of a performance of a compressor of the gas turbine, a
power output of the gas turbine, a heat rate of the gas turbine,
and a fuel consumption of the gas turbine.
[0006] In yet another embodiment, one or more non-transitory
computer-readable storage media has computer-executable
instructions embodied thereon, wherein when executed by at least
one processor, the computer-executable instructions cause the
processor to store a plurality rule sets in the memory device
wherein the rule sets are relative to the output of the gas
turbine, the rule sets include at least one rule expressed as a
relational expression of a real-time data output relative to a
real-time data input, the relational expression being specific to
at least one of a performance of a compressor of the gas turbine, a
power output of the gas turbine, a heat rate of the gas turbine,
and a fuel consumption of the gas turbine. The computer-executable
instructions further cause the processor to receive real-time and
historical data inputs from a condition monitoring system
associated with the gas turbine, the data inputs relating to
parameters affecting at least one of the performance of the
compressor, the power output, the heat rate, and the fuel
consumption, determine initial values of at least one of the
performance of the compressor, the power output, the heat rate, and
the fuel consumption, periodically determine current values of at
least one of the performance of the compressor, the power output,
the heat rate, and the fuel consumption, compare the determined
initial values to respective ones of the current values, determine
a degradation over time of the at least one of the performance of
the compressor, the power output, the heat rate, and the fuel
consumption based on the comparison, and recommend to an operator
of the gas turbine a set of corrective actions to correct the
degradation.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIGS. 1-8 show exemplary embodiments of the method and
system described herein.
[0008] FIG. 1 is a schematic block diagram of a remote monitoring
and diagnostic system in accordance with an exemplary embodiment of
the present invention;
[0009] FIG. 2 is a block diagram of an exemplary embodiment of a
network architecture of a local industrial plant monitoring and
diagnostic system, such as a distributed control system (DCS);
[0010] FIG. 3 is a block diagram of an exemplary rule set that may
be used with LMDS shown in FIG. 1;
[0011] FIG. 4 is a flow diagram of a method of determining an axial
compressor efficiency and degradation in performance over time in
accordance with an exemplary embodiment of the present
disclosure;
[0012] FIG. 5 is a flow diagram of a method of determining an axial
compressor flow and degradation in flow over time in accordance
with an exemplary embodiment of the present disclosure;
[0013] FIG. 6 is a flow diagram of a method of determining an
output power and degradation in power output over time in
accordance with an exemplary embodiment of the present
disclosure;
[0014] FIG. 7 is a flow diagram of a method of determining an
output power and degradation in power output over time in
accordance with an exemplary embodiment of the present disclosure;
and
[0015] FIG. 8 is a flow diagram of a method of a rule set used in
determining a gas turbine fuel consumption in accordance with an
exemplary embodiment of the present disclosure.
[0016] Although specific features of various embodiments may be
shown in some drawings and not in others, this is for convenience
only. Any feature of any drawing may be referenced and/or claimed
in combination with any feature of any other drawing.
DETAILED DESCRIPTION OF THE INVENTION
[0017] The following detailed description illustrates embodiments
of the invention by way of example and not by way of limitation. It
is contemplated that the invention has general application to
analytical and methodical embodiments of monitoring equipment
operation in industrial, commercial, and residential
applications.
[0018] Correcting gas turbine output degradation is a constant goal
of operators of machinery. However, output degradation is often the
result of degradation of input parameters. The methods described
herein facilitate identifying the root cause of this degradation.
The methods are used to monitor in real time the whole machine and
links components to each other. Troubleshooting flowcharts based on
output and input conditions can then be constructed.
[0019] A real time thermodynamic simulation software is used to
perform the methods and to improve accuracy of the assessments by
taking into account the specific design parameters of the monitored
gas turbine, and is not generic for all gas turbines. Thus, the
results fit better actual output values. Second, the thermodynamic
simulation software algorithms use empirical and statistical
correlations to estimate unknown parameters or correction factors.
For example, a temperature-independent specific heat ratio is not
assumed when calculating the axial compressor efficiency as is
commonly done. Rather, an empirical correlation is performed to
find the temperature at which this ratio is evaluated. Inlet guide
vane opening and speed change parameters are also corrected for.
Rules that fit each control zone, whether the machine is controlled
by speed or IGV opening based on how the gas turbine is controlled
when loading or unloading.
[0020] The degradation algorithm considers the initial condition of
the machine when first deploying the monitoring system and uses the
initial condition as a reference, rather than using theoretical
expected values as a reference that may or may not correspond to
the monitored machine. Moreover, the methodology uses rules and
algorithms that work at all load conditions and not only full load
conditions. However, output degradation can only be evaluated at
full load conditions, because power is input to the process as
demanded by the driven equipment, and it makes no sense to monitor
this value at partial load as the drop or increase in power may
represent a change in demand and not performance degradation. On
the other hand, the fuel consumption rule can be applied at full
and part load conditions. The rules validate input data to ensure
the associated sensors are working properly. Also, the calculated
fuel flow may be used to validate the measured value and the
calculated output power is used to determine whether the
torquemeter is malfunctioning by performing a heat balance
calculation, and by using a statistical correlation between the
output power and certain input parameters.
[0021] Performance monitoring is important to increasing
efficiency, reduce fuel costs, and predict fouling. The axial
compressor is the main source for many gas turbine performance
issues. Embodiments of the disclose described below monitor the
axial compressor polytropic efficiency and flow efficiency and
report degradation relative to initial reference conditions. The
efficiency calculations use empirical correlations to evaluate the
specific heat ratio. Also, corrections are made to account for the
gas generator speed, ambient temperature, as well as the inlet
guide vane opening. Flow calculations are also corrected to ISO and
full speed conditions. This methodology provides accurate results
and allows for degradation detection at an early stage. The
methodology also monitors output performance; mainly output power
and heat rate. Both are calculated at full load conditions and
corrected to ISO conditions. Degradation is defined relative to
initial reference conditions. Moreover, the methodology provides a
fuel consumption rule that is also applicable at part load
conditions. Fuel consumption is directly correlated to output power
and, thus, provides an output performance monitoring even at
part-load conditions.
[0022] An axial compressor performance rule-based monitoring module
as part of an online gas turbine performance monitoring system
includes:
[0023] 1. Axial compressor efficiency and flow, which are two
important parameters for characterizing the performance of an axial
compressor in a gas turbine and can be used as a degradation
indicator. The polytropic efficiency is referred to as small-stage
efficiency and represents the isentropic efficiency of an elemental
stage. In calculating this efficiency, the ratio of specific heats
is calculated using an empirical correlation to capture the
dependence on temperature. The calculated efficiency is then
corrected for the actual axial compressor speed and inlet guide
vane opening. The correction coefficients are obtained using a
thermodynamic simulations software. This efficiency is monitored
for degradation relative to the initial efficiency calculated at
the first deployment of the monitoring system. Monitoring this
value over time permits a prediction of the need for water washing
or the presence of compressor fouling. The flow is proportional to
the discharge pressure and depends on the ambient temperature and
pressure. After correcting the ambient conditions to ISO
conditions, the flow efficiency is also corrected based on the
actual speed and inlet guide vane opening.
[0024] 2. Output power and heat rate. The power output is either
read from a torquemeter and, if not available, an energy balance is
used to estimate it. A change in ambient conditions and both inlet
and exit pressure losses are corrected for. These correction
factors are obtained using thermodynamic simulations. The
degradation of the corrected power or heat rate at base load
conditions is monitored. The degradation factors are defined based
on initial conditions similar to the definition of the axial
compressor efficiency.
[0025] 3. Fuel consumption: While the output rules apply at base
load conditions, this rule can be applied at base load and part
load conditions. Fuel consumption is the product of the fuel flow
rate and the lower heating value of the fuel. Fuel consumption is a
linear function of load at ISO conditions. The fuel flow is
measured, the fuel flow and power are corrected to ISO conditions,
and then the results are compared to the expected fuel consumption
at ISO. The deviation in this case is the degradation parameter
that can be monitored over time. If a reliable measurement of fuel
flow is not available, the fuel flow is calculated using the
choking condition on the gas control valve.
[0026] FIG. 1 is a schematic block diagram of remote monitoring and
diagnostic system 100 in accordance with an exemplary embodiment of
the present invention. In the exemplary embodiment, system 100
includes a remote monitoring and diagnostic center 102. Remote
monitoring and diagnostic center 102 is operated by an entity, such
as, an OEM of a plurality of equipment purchased and operated by a
separate business entity, such as, an operating entity. In the
exemplary embodiment, the OEM and operating entity enter into a
support arrangement whereby the OEM provides services related to
the purchased equipment to the operating entity. The operating
entity may own and operate purchased equipment at a single site or
multiple sites. Moreover, the OEM may enter into support
arrangements with a plurality of operating entities, each operating
their own single site or multiple sites. The multiple sites each
may contain identical individual equipment or pluralities of
identical sets of equipment, such as trains of equipment.
Additionally, at least some of the equipment may be unique to a
site or unique to all sites.
[0027] In the exemplary embodiment, a first site 104 includes one
or more process analyzers 106, equipment monitoring systems 108,
equipment local control centers 110, and/or monitoring and alarm
panels 112 each configured to interface with respective equipment
sensors and control equipment to effect control and operation of
the respective equipment. The one or more process analyzers 106,
equipment monitoring systems 108, equipment local control centers
110, and/or monitoring and alarm panels 112 are communicatively
coupled to an intelligent monitoring and diagnostic system 114
through a network 116. Intelligent monitoring and diagnostic (IMAD)
system 114 is further configured to communicate with other on-site
systems (not shown in FIG. 1) and offsite systems, such as, but not
limited to, remote monitoring and diagnostic center 102. In various
embodiments, IMAD 114 is configured to communicate with remote
monitoring and diagnostic center 102 using for example, a dedicated
network 118, a wireless link 120, and the Internet 122.
[0028] Each of a plurality of other sites, for example, a second
site 124 and an nth site 126 may be substantially similar to first
site 104 although may or may not be exactly similar to first site
104.
[0029] FIG. 2 is a block diagram of an exemplary embodiment of a
network architecture 200 of a local industrial plant monitoring and
diagnostic system, such as a distributed control system (DCS) 201.
The industrial plant may include a plurality of plant equipment,
such as gas turbines, centrifugal compressors, gearboxes,
generators, pumps, motors, fans, and process monitoring sensors
that are coupled in flow communication through interconnecting
piping, and coupled in signal communication with DCS 201 through
one or more remote input/output (I/O) modules and interconnecting
cabling and/or wireless communication. In the exemplary embodiment,
the industrial plant includes DCS 201 including a network backbone
203. Network backbone 203 may be a hardwired data communication
path fabricated from twisted pair cable, shielded coaxial cable or
fiber optic cable, for example, or may be at least partially
wireless. DCS 201 may also include a processor 205 that is
communicatively coupled to the plant equipment, located at the
industrial plant site or at remote locations, through network
backbone 203. It is to be understood that any number of machines
may be operatively connected to network backbone 203. A portion of
the machines may be hardwired to network backbone 203, and another
portion of the machines may be wirelessly coupled to backbone 203
via a wireless base station 207 that is communicatively coupled to
DCS 201. Wireless base station 207 may be used to expand the
effective communication range of DCS 201, such as with equipment or
sensors located remotely from the industrial plant but, still
interconnected to one or more systems within the industrial
plant.
[0030] DCS 201 may be configured to receive and display operational
parameters associated with a plurality of equipment, and to
generate automatic control signals and receive manual control
inputs for controlling the operation of the equipment of industrial
plant. In the exemplary embodiment, DCS 201 may include a software
code segment configured to control processor 205 to analyze data
received at DCS 201 that allows for on-line monitoring and
diagnosis of the industrial plant machines. Data may be collected
from each machine, including gas turbines, centrifugal compressors,
pumps and motors, associated process sensors, and local
environmental sensors including, for example, vibration, seismic,
temperature, pressure, current, voltage, ambient temperature and
ambient humidity sensors. The data may be pre-processed by a local
diagnostic module or a remote input/output module, or may
transmitted to DCS 201 in raw form.
[0031] A local monitoring and diagnostic system (LMDS) 213 may be a
separate add-on hardware device, such as, for example, a personal
computer (PC), that communicates with DCS 201 and other control
systems 209 and data sources through network backbone 203. LMDS 213
may also be embodied in a software program segment executing on DCS
201 and/or one or more of the other control systems 209.
Accordingly, LMDS 213 may operate in a distributed manner, such
that a portion of the software program segment executes on several
processors concurrently. As such, LMDS 213 may be fully integrated
into the operation of DCS 201 and other control systems 209. LMDS
213 analyzes data received by DCS 201, data sources, and other
control systems 209 to determine an operational health of the
machines and/or a process employing the machines using a global
view of the industrial plant.
[0032] In the exemplary embodiment, network architecture 100
includes a server grade computer 202 and one or more client systems
203. Server grade computer 202 further includes a database server
206, an application server 208, a web server 210, a fax server 212,
a directory server 214, and a mail server 216. Each of servers 206,
208, 210, 212, 214, and 216 may be embodied in software executing
on server grade computer 202, or any combinations of servers 206,
208, 210, 212, 214, and 216 may be embodied alone or in combination
on separate server grade computers coupled in a local area network
(LAN) (not shown). A data storage unit 220 is coupled to server
grade computer 202. In addition, a workstation 222, such as a
system administrator's workstation, a user workstation, and/or a
supervisor's workstation are coupled to network backbone 203.
Alternatively, workstations 222 are coupled to network backbone 203
using an Internet link 226 or are connected through a wireless
connection, such as, through wireless base station 207.
[0033] Each workstation 222 may be a personal computer having a web
browser. Although the functions performed at the workstations
typically are illustrated as being performed at respective
workstations 222, such functions can be performed at one of many
personal computers coupled to network backbone 203. Workstations
222 are described as being associated with separate exemplary
functions only to facilitate an understanding of the different
types of functions that can be performed by individuals having
access to network backbone 203.
[0034] Server grade computer 202 is configured to be
communicatively coupled to various individuals, including employees
228 and to third parties, e.g., service providers 230. The
communication in the exemplary embodiment is illustrated as being
performed using the Internet, however, any other wide area network
(WAN) type communication can be utilized in other embodiments,
i.e., the systems and processes are not limited to being practiced
using the Internet.
[0035] In the exemplary embodiment, any authorized individual
having a workstation 232 can access LMDS 213. At least one of the
client systems may include a manager workstation 234 located at a
remote location. Workstations 222 may be embodied on personal
computers having a web browser. Also, workstations 222 are
configured to communicate with server grade computer 202.
Furthermore, fax server 212 communicates with remotely located
client systems, including a client system 236 using a telephone
link (not shown). Fax server 212 is configured to communicate with
other client systems 228, 230, and 234, as well.
[0036] Computerized modeling and analysis tools of LMDS 213, as
described below in more detail, may be stored in server 202 and can
be accessed by a requester at any one of client systems 204. In one
embodiment, client systems 204 are computers including a web
browser, such that server grade computer 202 is accessible to
client systems 204 using the Internet. Client systems 204 are
interconnected to the Internet through many interfaces including a
network, such as a local area network (LAN) or a wide area network
(WAN), dial-in-connections, cable modems and special high-speed
ISDN lines. Client systems 204 could be any device capable of
interconnecting to the Internet including a web-based phone,
personal digital assistant (PDA), or other web-based connectable
equipment. Database server 206 is connected to a database 240
containing information about industrial plant 10, as described
below in greater detail. In one embodiment, centralized database
240 is stored on server grade computer 202 and can be accessed by
potential users at one of client systems 204 by logging onto server
grade computer 202 through one of client systems 204. In an
alternative embodiment, database 240 is stored remotely from server
grade computer 202 and may be non-centralized.
[0037] Other industrial plant systems may provide data that is
accessible to server grade computer 202 and/or client systems 204
through independent connections to network backbone 204. An
interactive electronic tech manual server 242 services requests for
machine data relating to a configuration of each machine. Such data
may include operational capabilities, such as pump curves, motor
horsepower rating, insulation class, and frame size, design
parameters, such as dimensions, number of rotor bars or impeller
blades, and machinery maintenance history, such as field
alterations to the machine, as-found and as-left alignment
measurements, and repairs implemented on the machine that do not
return the machine to its original design condition.
[0038] A portable vibration monitor 244 may be intermittently
coupled to LAN directly or through a computer input port such as
ports included in workstations 222 or client systems 204.
Typically, vibration data is collected in a route, collecting data
from a predetermined list of machines on a periodic basis, for
example, monthly or other periodicity. Vibration data may also be
collected in conjunction with troubleshooting, maintenance, and
commissioning activities. Further, vibration data may be collected
continuously in a real-time or near real-time basis. Such data may
provide a new baseline for algorithms of LMDS 213. Process data may
similarly, be collected on a route basis or during troubleshooting,
maintenance, and commissioning activities. Moreover, some process
data may be collected continuously in a real-time or near real-time
basis. Certain process parameters may not be permanently
instrumented and a portable process data collector 245 may be used
to collect process parameter data that can be downloaded to DCS 201
through workstation 222 so that it is accessible to LMDS 213. Other
process parameter data, such as process fluid composition analyzers
and pollution emission analyzers may be provided to DCS 201 through
a plurality of on-line monitors 246.
[0039] Electrical power supplied to various machines or generated
by generated by generators with the industrial plant may be
monitored by a motor protection relay 248 associated with each
machine. Typically, such relays 248 are located remotely from the
monitored equipment in a motor control center (MCC) or in
switchgear 250 supplying the machine. In addition, to protection
relays 248, switchgear 250 may also include a supervisory control
and data acquisition system (SCADA) that provides LMDS 213 with
power supply or power delivery system (not shown) equipment located
at the industrial plant, for example, in a switchyard, or remote
transmission line breakers and line parameters.
[0040] FIG. 3 is a block diagram of an exemplary rule set 280 that
may be used with LMDS 213 (shown in FIG. 1). Rule set 280 may be a
combination of one or more custom rules, and a series of properties
that define the behavior and state of the custom rules. The rules
and properties may be bundled and stored in a format of an XML
string, which may be encrypted based on a 25 character alphanumeric
key when stored to a file. Rule set 280 is a modular knowledge cell
that includes one or more inputs 282 and one or more outputs 284.
Inputs 282 may be software ports that direct data from specific
locations in LMDS 213 to rule set 280. For example, an input from a
pump outboard vibration sensor may be transmitted to a hardware
input termination in DCS 201. DCS 201 may sample the signal at that
termination to receive the signal thereon. The signal may then be
processed and stored at a location in a memory accessible and/or
integral to DCS 201. A first input 286 of rule set 280 may be
mapped to the location in memory such that the contents of the
location in memory is available to rule set 280 as an input.
Similarly, an output 288 may be mapped to another location in the
memory accessible to DCS 201 or to another memory such that the
location in memory contains the output 288 of rule set 280.
[0041] In the exemplary embodiment, rule set 280 includes one or
more rules relating to monitoring and diagnosis of specific
problems associated with equipment operating in an industrial
plant, such as, for example, a gas reinjection plant, a liquid
natural gas (LNG) plant, a power plant, a refinery, and a chemical
processing facility. Although rule set 280 is described in terms of
being used with an industrial plant, rule set 280 may be
appropriately constructed to capture any knowledge and be used for
determining solutions in any field. For example, rule set 280 may
contain knowledge pertaining to economic behavior, financial
activity, weather phenomenon, and design processes. Rule set 280
may then be used to determine solutions to problems in these
fields. Rule set 280 includes knowledge from one or many sources,
such that the knowledge is transmitted to any system where rule set
280 is applied. Knowledge is captured in the form of rules that
relate outputs 284 to inputs 282 such that a specification of
inputs 282 and outputs 284 allows rule set 280 to be applied to
LMDS 213. Rule set 280 may include only rules specific to a
specific plant asset and may be directed to only one possible
problem associated with that specific plant asset. For example,
rule set 280 may include only rules that are applicable to a motor
or a motor/pump combination. Rule set 280 may only include rules
that determine a health of the motor/pump combination using
vibration data. Rule set 280 may also include rules that determine
the health of the motor/pump combination using a suite of
diagnostic tools that include, in addition to vibration analysis
techniques, but may also include, for example, performance
calculational tools and/or financial calculational tools for the
motor/pump combination.
[0042] In operation, rule set 280 is created in a software
developmental tool that prompts a user for relationships between
inputs 282 and outputs 284. Inputs 282 may receive data
representing, for example digital signals, analog signals,
waveforms, processed signals, manually entered and/or configuration
parameters, and outputs from other rule sets. Rules within rule set
280 may include logical rules, numerical algorithms, application of
waveform and signal processing techniques, expert system and
artificial intelligence algorithms, statistical tools, and any
other expression that may relate outputs 284 to inputs 282. Outputs
284 may be mapped to respective locations in the memory that are
reserved and configured to receive each output 284. LMDS 213 and
DCS 201 may then use the locations in memory to accomplish any
monitoring and/or control functions LMDS 213 and DCS 201 may be
programmed to perform. The rules of rule set 280 operate
independently of LMDS 213 and DCS 201, although inputs 282 may be
supplied to rule set 280 and outputs 284 may be supplied to rule
set 280, directly or indirectly through intervening devices.
[0043] During creation of rule set 280, a human expert in the field
divulges knowledge of the field particular to a specific asset
using a development tool by programming one or more rules. The
rules are created by generating expressions of relationship between
outputs 284 and inputs 282. Operands may be selected from a library
of operands, using graphical methods, for example, using drag and
drop on a graphical user interface built into the development tool.
A graphical representation of an operand may be selected from a
library portion of a screen display (not shown) and dragged and
dropped into a rule creation portion. Relationships between input
282 and operands are arranged in a logical display fashion and the
user is prompted for values, such as, constants, when appropriate
based on specific operands and specific ones of inputs 282 that are
selected. As many rules that are needed to capture the knowledge of
the expert are created. Accordingly, rule set 280 may include a
robust set of diagnostic and/or monitoring rules or a relatively
less robust set of diagnostic and/or monitoring rules based on a
customer's requirements and a state of the art in the particular
field of rule set 280. The development tool provides resources for
testing rule set 280 during the development to ensure various
combinations and values of inputs 282 produce expected outputs at
outputs 284.
[0044] As described below, rule sets are defined to assess the
performance of axial compressor efficiency, axial compressor
efficiency flow, gas turbine power output, and gas turbine heat
rate. The measurements used in the determination include ambient
temperature and pressure, GT axial compressor inlet temperature and
pressure, GT axial compressor discharge temperature and pressure,
GT inlet losses, GT axial compressor speed (TNH) and GT power
turbine speed (TNL), power output (from torquemeter or driven
compressor thermodynamic balance), and fuel flow and fuel
composition.
[0045] FIG. 4 is a flow diagram of a method 400 of determining an
axial compressor efficiency and degradation in performance over
time in accordance with an exemplary embodiment of the present
disclosure. In the exemplary embodiment, method 400 includes
determining that the gas turbine is at steady state 402 and that
the inlet guide vanes position is greater than 55% open 404.
Temperatures T.sub.2 and T.sub.3 are read 406 from the monitoring
system. Given the measured T.sub.3, evaluate 408 the ambient
corrected T.sub.3corr as:
T.sub.3corr=T.sub.3+f.sub.T3(T.sub.2),
where f.sub.T3(T.sub.2) is the correction based on ambient
temperature, defined 410 as:
f.sub.T3(T.sub.2)=c.sub.0+c.sub.1(T.sub.2).sub..degree.
F.+c.sub.2(T.sub.2).sub..degree.
F..sup.2+c.sub.3(T.sub.2).sub..degree. F..sup.3,
where c.sub.0 . . . c.sub.3 are constants and T.sub.3corr is the
temperature at which the ratio of specific heats .gamma. is
evaluated 412:
.gamma. ( ? ) = c 0 + c 1 ( ? ? ) ? + c 2 ( ? ? ) ? + ? ( ? ? ) ? ,
? indicates text missing or illegible when filed ##EQU00001##
where c.sub.0 . . . c.sub.3 are different constants then above.
[0046] Polytropic efficiency is evaluated as 414:
.eta. = .gamma. ( ? ) - 1 .gamma. ( ? ) * ln ( ? P 2 ) ? ln ( ? T 2
) ? * K 1 ( T amb , THN ) * K 2 ( T amb , IGV ) ##EQU00002## ?
indicates text missing or illegible when filed ##EQU00002.2##
[0047] Further corrections are applied in order to estimate
efficiency in the ranges: 94%<GT axial compressor speed
(TNH)<100% and 56.degree.<IGV<85.degree..
[0048] From base load (Temperature control, TNH=100%, IGV=85)
downward, the control is operated by acting on TNH (from 100% to
94%) and keeping constant the IGV at 85.degree..
[0049] Ambient temperature corrected TNH correlation (K.sub.1) is
from 5.degree. F. to 140.degree. F.:
T.sub.amb parameter is normalized as:
? = ( T amb ? ) ? ##EQU00003## ? indicates text missing or
illegible when filed ##EQU00003.2##
TNH parameter is between 0.94 and 1.
[0050] When TNH reaches 94%, further load decreasing is obtained by
reducing IGV aperture (from 85.degree. to 56.degree.) and keeping
constant the THN at 94%.
[0051] Ambient temperature corrected IGV correlation (K.sub.2) is
from 5.degree. F. to 140.degree. F.:
T.sub.amb parameter is normalized as:
? = ( T amb ? ) ? ##EQU00004## ? indicates text missing or
illegible when filed ##EQU00004.2##
IGV parameter is normalized as:
IGV param = IGV 85 ##EQU00005##
[0052] Method 400 includes correcting 416 .eta. using K1 and K2,
buffering 418 values and determining 420 an average efficiency. The
degradation in compressor efficiency is determined 422 using:
Degradation = ( 1 - ? FirstTimeEff ) * 100 ##EQU00006## ? indicates
text missing or illegible when filed ##EQU00006.2##
[0053] FIG. 5 is a flow diagram of a method 500 of determining an
axial compressor flow and degradation in flow over time in
accordance with an exemplary embodiment of the present
disclosure.
[0054] The general formula of flow estimation is:
W 2 = ? ( T amb 518.67 ) ? ( ? 14.6959 ) ? * K 1 ( T amb , TNH ) *
K 2 ( T amb , IGV ) ##EQU00007## ? indicates text missing or
illegible when filed ##EQU00007.2##
[0055] In the exemplary embodiment, method 500 includes determining
that the gas turbine is at steady state 502 and that the inlet
guide vanes position is greater than 55% open 504.
[0056] Method 500 includes reading 506 T2 in .degree. K., P2, and
compressor discharge pressure (CDP) in absolute pressure units. A
flow coefficient is determined 508 from:
Flow Coefficient = CDP * ? ? ##EQU00008## ? indicates text missing
or illegible when filed ##EQU00008.2##
[0057] The flow coefficient is corrected 510 using the ambient
temperature corrected TNH correlation K.sub.1 and the ambient
temperature corrected IGV correlation K.sub.2:
Flow Coefficient.sub.Corrected=Flow Coefficient*K1*K2
[0058] Method 500 includes buffering 512 values and determining 514
an average flow efficiency. The degradation in flow efficiency is
determined 516 using:
Degradation = ( 1 - ? FirstTimeCoeff ) * 100 ##EQU00009## ?
indicates text missing or illegible when filed ##EQU00009.2##
[0059] FIG. 6 is a flow diagram of a method 600 of determining an
output power and degradation in power output over time in
accordance with an exemplary embodiment of the present disclosure.
In the exemplary embodiment, method 600 includes determining that
the gas turbine is at steady state 602, that the inlet guide vanes
position 604 is greater than 84% open, and that GT axial compressor
speed (TNH) 606 is greater than 98%.
[0060] Values are read 608 for Pamb in units of psi, Tamb in units
of .degree. F., .DELTA.P inlet in mm H2O, RH (humidity) in units of
percent, and TNL in units of percent. Correction factors are
calculated 610 using:
K(Pamb)*K(RH)*K*(.DELTA.P inlet)*K(Tamb, TNL)
[0061] Power output is read 612 from one of more of a torque meter,
a heat balance, or from absorbed power from a centrifugal
compressor plus losses and a corrected power output is determined
614 using:
Output Power Corrected=Output Power/Correction Factors [0062]
Degradation of the output power over time is determined 616
using:
[0062] Degradation = ( 1 - ? ? ) * 100 ##EQU00010## ? indicates
text missing or illegible when filed ##EQU00010.2##
[0063] FIG. 7 is a flow diagram of a method 700 of determining an
output power and degradation in power output over time in
accordance with an exemplary embodiment of the present disclosure.
In the exemplary embodiment, method 700 includes determining that
the gas turbine is at steady state 702, that the inlet guide vanes
position 704 is greater than 84% open, and that GT axial compressor
speed (TNH) 706 is greater than 98%.
[0064] Values are read 708 for Pamb in units of psi, Tamb in units
of .degree. F., .DELTA.P inlet in mm H2O, RH (humidity) in units of
percent, and TNL in units of percent. Correction factors are
calculated 710 using:
K(Pamb)*K(RH)*K*(.DELTA.P inlet)*K(Tamb, TNL) [0065] A heat rate is
determined 712 using:
[0065] (Fuel Flow*LHV)/(Out Power) [0066] A corrected heat rate is
determined 714 using:
[0066] Heat Rate Corrected=Heat Rate/Correction Factors [0067]
Degradation of the output power over time is determined 716
using:
[0067] Degradation = ( 1 + ? ? ) * 100 ##EQU00011## ? indicates
text missing or illegible when filed ##EQU00011.2##
[0068] FIG. 8 is a flow diagram of a method 800 of a rule set used
in determining a gas turbine fuel consumption in accordance with an
exemplary embodiment of the present disclosure. In the exemplary
embodiment, method 800 includes determining that the gas turbine is
at steady state 802. If yes, method 800 includes receiving 804 a
measured fuel flow from, for example, a fuel meter and a calculated
fuel rate from a fuel rate calculation and determining 806 that the
measured and the calculated are within 10% of the calculated value
with respect to each other using:
abs(Measured-Calculated)/Calculated<10%
[0069] If yes 808, the measured fuel flow is used below. If no 810,
the calculated fuel flow is used below. A fuel consumption
correction is determined using:
Fuel Consumption Corrections=Fuel Flow*LHV/Correction Factors
[0070] The correction factors are determined from values read 814
for Pamb in units of psi, Tamb in units of .degree. F., .DELTA.P
inlet in mm H2O, RH (humidity) in units of percent, TNL in units of
percent, and power output in units of kilowatts (kW). Correction
factors are calculated 816 using:
K(Pamb)*K(RH)*K*(.DELTA.P inlet)*K(Tamb, TNL, Output Power)
[0071] The fuel consumption correction at isometric power is
determined 818 using:
Fuel Consumption Correction at ISO Power=Fuel Consumption
Correction/Power Correction
[0072] The power correction ratio determined from a calculated
expected isometric fuel consumption at current power 820 and a
calculated expected isometric fuel consumption at ISO power
822.
[0073] The fuel consumption correction at isometric power is
buffered 826 for a predetermined period, for example, but not
limited to, sixty minutes to determine 828 an actual isometric fuel
consumption. The calculated expected isometric fuel consumption at
ISO power 822 is also buffered 830 for a predetermined period and
the expected isometric fuel consumption is determined 832. A fuel
consumption deviation is determined 834 using:
Deviation=(actual isometric fuel consumption 828)-(expected
isometric fuel consumption 832)
[0074] The logic flows depicted in the figures do not require the
particular order shown, or sequential order, to achieve desirable
results. In addition, other steps may be provided, or steps may be
eliminated, from the described flows, and other components may be
added to, or removed from, the described systems. Accordingly,
other embodiments are within the scope of the following claims.
[0075] It will be appreciated that the above embodiments that have
been described in particular detail are merely example or possible
embodiments, and that there are many other combinations, additions,
or alternatives that may be included.
[0076] Also, the particular naming of the components,
capitalization of terms, the attributes, data structures, or any
other programming or structural aspect is not mandatory or
significant, and the mechanisms that implement the invention or its
features may have different names, formats, or protocols. Further,
the system may be implemented via a combination of hardware and
software, as described, or entirely in hardware elements. Also, the
particular division of functionality between the various system
components described herein is merely one example, and not
mandatory; functions performed by a single system component may
instead be performed by multiple components, and functions
performed by multiple components may instead performed by a single
component.
[0077] Some portions of above description present features in terms
of algorithms and symbolic representations of operations on
information. These algorithmic descriptions and representations may
be used by those skilled in the data processing arts to most
effectively convey the substance of their work to others skilled in
the art. These operations, while described functionally or
logically, are understood to be implemented by computer programs.
Furthermore, it has also proven convenient at times, to refer to
these arrangements of operations as modules or by functional names,
without loss of generality.
[0078] Unless specifically stated otherwise as apparent from the
above discussion, it is appreciated that throughout the
description, discussions utilizing terms such as "processing" or
"computing" or "calculating" or "determining" or "displaying" or
"providing" or the like, refer to the action and processes of a
computer system, or similar electronic computing device, that
manipulates and transforms data represented as physical
(electronic) quantities within the computer system memories or
registers or other such information storage, transmission or
display devices.
[0079] While the disclosure has been described in terms of various
specific embodiments, it will be recognized that the disclosure can
be practiced with modification within the spirit and scope of the
claims.
[0080] The term processor, as used herein, refers to central
processing units, microprocessors, microcontrollers, reduced
instruction set circuits (RISC), application specific integrated
circuits (ASIC), logic circuits, and any other circuit or processor
capable of executing the functions described herein.
[0081] As used herein, the terms "software" and "firmware" are
interchangeable, and include any computer program stored in memory
for execution by processor 205, including RAM memory, ROM memory,
EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory.
The above memory types are exemplary only, and are thus not
limiting as to the types of memory usable for storage of a computer
program.
[0082] As will be appreciated based on the foregoing specification,
the above-described embodiments of the disclosure may be
implemented using computer programming or engineering techniques
including computer software, firmware, hardware or any combination
or subset thereof, wherein the technical effect includes (a)
storing a plurality rule sets in the memory device, the rule sets
relative to the output of the gas turbine, the rule sets including
at least one rule expressed as a relational expression of a
real-time data output relative to a real-time data input, the
relational expression being specific to at least one of a
performance of a compressor of the gas turbine, a power output of
the gas turbine, a heat rate of the gas turbine, and a fuel
consumption of the gas turbine (b) receiving real-time and
historical data inputs from a condition monitoring system
associated with the gas turbine, the data inputs relating to
parameters affecting at least one of the performance of the
compressor, the power output, the heat rate, and the fuel
consumption, (c) determining initial values of at least one of the
performance of the compressor, the power output, the heat rate, and
the fuel consumption, (d) periodically determining current values
of at least one of the performance of the compressor, the power
output, the heat rate, and the fuel consumption, (e) comparing the
determined initial values to respective ones of the current values,
(f) determining a degradation over time of the at least one of the
performance of the compressor, the power output, the heat rate, and
the fuel consumption based on the comparison, (g) recommending to
an operator of the gas turbine a set of corrective actions to
correct the degradation, (h) determining unknown or unsensed
parameters and correction factors using a thermodynamic simulation
algorithm, (i) verifying an operability of sensors providing the
real-time data inputs using at least one of the historical data
inputs and the thermodynamic simulation algorithm, (j) determining
a axial compressor efficiency using a temperature dependent
specific heat ratio, (k) determining a temperature at which the
ratio is evaluated using an empirical correlation, (l) monitoring
the axial compressor polytropic efficiency and flow efficiency, (m)
correcting the determined efficiency for an actual speed of the
axial compressor and an actual opening of an inlet guide vane, (n)
determining correction coefficients using a thermodynamic
simulation, (o) correcting ambient conditions to isometric
conditions, (p) correcting the flow efficiency based on the actual
axial compressor speed and the inlet guide vane opening, (q)
receiving a power output signal from a torquemeter, (r) determining
the power output using an energy balance algorithm to generate a
calculate power output signal, (s) determining values of the fuel
consumption using a fuel flow rate and a lower heating value of the
fuel, and (t) determining values of the fuel consumption at least
one of full load and partial load. Any such resulting program,
having computer-readable code means, may be embodied or provided
within one or more computer-readable media, thereby making a
computer program product, i.e., an article of manufacture,
according to the discussed embodiments of the disclosure. The
computer readable media may be, for example, but is not limited to,
a fixed (hard) drive, diskette, optical disk, magnetic tape,
semiconductor memory such as read-only memory (ROM), and/or any
transmitting/receiving medium such as the Internet or other
communication network or link. The article of manufacture
containing the computer code may be made and/or used by executing
the code directly from one medium, by copying the code from one
medium to another medium, or by transmitting the code over a
network.
[0083] Many of the functional units described in this specification
have been labeled as modules, in order to more particularly
emphasize their implementation independence. For example, a module
may be implemented as a hardware circuit comprising custom very
large scale integration ("VLSI") circuits or gate arrays,
off-the-shelf semiconductors such as logic chips, transistors, or
other discrete components. A module may also be implemented in
programmable hardware devices such as field programmable gate
arrays (FPGAs), programmable array logic, programmable logic
devices (PLDs) or the like.
[0084] Modules may also be implemented in software for execution by
various types of processors. An identified module of executable
code may, for instance, comprise one or more physical or logical
blocks of computer instructions, which may, for instance, be
organized as an object, procedure, or function. Nevertheless, the
executables of an identified module need not be physically located
together, but may comprise disparate instructions stored in
different locations which, when joined logically together, comprise
the module and achieve the stated purpose for the module.
[0085] A module of executable code may be a single instruction, or
many instructions, and may even be distributed over several
different code segments, among different programs, and across
several memory devices. Similarly, operational data may be
identified and illustrated herein within modules, and may be
embodied in any suitable form and organized within any suitable
type of data structure. The operational data may be collected as a
single data set, or may be distributed over different locations
including over different storage devices, and may exist, at least
partially, merely as electronic signals on a system or network.
[0086] The above-described embodiments of a method and online gas
turbine performance monitoring system that includes a rule module
provides a cost-effective and reliable means for providing
meaningful operational recommendations and troubleshooting actions.
Moreover, the system is more accurate and less prone to false
alarms. More specifically, the methods and systems described herein
can predict component failure at a much earlier stage than known
systems to facilitate significantly reducing outage time and
preventing trips. In addition, the above-described methods and
systems facilitate predicting anomalies at an early stage enabling
site personnel to prepare and plan for a shutdown of the equipment.
As a result, the methods and systems described herein facilitate
operating gas turbines and other equipment in a cost-effective and
reliable manner.
[0087] 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 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 languages of the claims.
* * * * *