Performance Assessment and Statistical Process Control: An Approach to Operation and Maintenance Cost Reduction

Copyright © IFAC Energy Systems, Management and Economics, T okyo, J apan 1989

PO W ER SYSTE M O PERAT ION

PERFORMANCE ASSESSMENT AND STATISTICAL PROCESS CONTROL: AN APPROACH TO OPERATION AND MAINTENANCE COST REDUCTION R. G. Kneile, G. L. Stephens and K. S. Vasudeva Bailey Controls Company, 2 9801 Euclid Ave., W ickliffe, OH 44092, USA

Abstract. Accurate and timely Information on the performance and condition of equipment Is vital to safe, reliable and economical operation of power plants. Early detection of performance degradation and Incipient failure Is critical to the process of averting catastrophic damage and unscheduled outages. The Utility Performance Assessment package developed by Bailey Controls Integrates performance assessment with digital distributed data acquisition and control systems to provide an overall Information management system to assist optimal control and maintenance management of plant operl!ltlons. Furthermore, Statistical Process Control techniques utilizing Shewhart Charts can be readily combined with the performance assessment package to detect plant abnormalities. It Is the purpose of this contribution to highlight the features of the performance assessment package and to demonstrate how Statistical Process Control techniques can be coupled with a performance assessment package to produce an effective o~ line tool detect equipment and reduce plant operation and maintenance costs. Keywords

Performance monitoring; statistical process control; statistics; utility power plant; data acquisition

INTRODUCTION

from a reference model and displayed to Indicate the capability of the equipment In the absence of degradation or mechanical problems. This expected performance Information can also be considered as an achievable target for plant management and operators.

The performance assessment package for utility power plants encompasses the boiler, the turbine, the condenser and the feedwater heaters. T he computing power of this package Is provided by multiple, multIfunction controller modules which are fully Integrated with the Bailey INFI ~NETWORK SO data acquisition and control system. This prov Ides the user with the advanced features of the INFI ~NETWORK SO data acquisition and control system Including graphic displays, data logging, trendlng, archiving and alarm management.

To assist Interpretation. the displays also provide Information on an equivalent loss In heat rate which Is determined from the difference between the actual and expected performance values. In addition, the equivalent loss In heat rate 15 converted Into an equivalent fuel cost, which permits plant personnel to readily access the magnitude of the economic loss due to less than expected operation.

A key feature of the performance assessment package Is Its ability to compute both actual and expected performance values for major components of the power plant. While these two performance values vary with load conditions, In the absence of p I ant degradation their ratio tends to remain statistically constant with load. This makes It possible to directly apply Statistical Process Control techniques to Identify abnormalities In the performance of plant equipment.

Upon Initializing the performance assessment package (and at any other tl me the user may cons Ider approprlatel, the parameters for the reference model are defined. This Is accomplished by operating the plant over the desired operating range and collecting data; the data collection 15 carried out automatically by the performance assessment package. A fter a sufficient number of data sets have been collected, a regression analysis of the data Is Initiated to estab IIsh the reference model.

This paper discusses some of the principal features of the performance assessment package. It also demonstrates how Statistical Process Control techniques such as the Shew hart C harts can be coupled with the performance assessment package to provide an effective o~lIne tool to detect equipment malfunction.

Some of the key features of the performance assessment package are summarized below: • automatic collection of data to estab \Ish the reference model, • definition of reference models via regression analysis, • o~lIne plant performance computations, • determination of expected plant performance and • displaying the results at the operator's console.

PERFORMANCE ASSESSMENT PACKAGE The philosophy of perform ance assessment goes beyond that for deterministic performance calculations. O~ line performance assessment provides an environment In which the calculated performance of each piece of equipment Is compared directly with Its expected behavlor under similar operating conditions. As a resu It, true changes In p I ant equ Ipment performance can be readily observed.

The performance assessment package completely and automatically Integrates these functions Into an easy to use package. T he computational models are generic w h Ic h perm It them to be app lied to most power p I ants. They are customiZed through a procedure whereby the configuration of the plant Is defined by generating data flies Interactively through user friendly menus.

The output displays provided by the performance assessment pack age prov Ide tw 0 types of perform ance Information; actual and expected. Actual values refer to current plant performance computed o~lIne. For comparison purposes, expected values are computed

To assist plant personnel In comprehending plant operating data, the performance assessment displays are classified under three basic categories.

2 15

R. G. Kneile. G. L. Stephens and K. S. Vasudeva

216 • • •

overll" plllnt performllnce essessment dlsplllY coniro"eble parllmeters dlsplllY deterlorllflon of plllnt components dlsplllY

Even though these dlsplllY clltegorles lire useful to 11" plllnt personnel, ellCh Is designed to IIld 11 specific group. P I lint menllgement personnel lire generlllly Infares18d In monitoring overllll plllnt performance to det1rmlne the status of plllnt operlltlon relative to previous operation. PllInt Operlltors lire generally concerned with monitoring coniro"eble pllr'lImeters so that they can operate the pi ant at Its most efflc lent operatlnmg point. Plan engineers are often most concerned with mon Itorlng the deterioration of p I lint components to assist them In scheduling plant maintenance most effectively. Perfocmorx;Q Models

The performance clllculatlons are based on the ASME power test codes. The follow Ing performance pllr'ameters are compu18d: Boiler: • boiler efficiency Turbine; • turb Ine efflc lency for ellCh section • turb Ine flow coefflc lent for ellCh section Condenser: • condenser heat 'transfer effectiveness F eedw at1r heater. • feedw lifer heater heat 'transfer effectiveness for the steam condensing section

heaters>, It Is defined using 11 set of basic unit modules which Include; INPUT STAGE HEATER NODE SPlITTER FWPT GENERATOR

Input sirellm Into the turbine cycle turbine stage heat exchanger generlll mat1rlel and energy balance module - splitting of one sireem Into severe I sireams - feedwat1r pumliturblne - turbine generator -

Fig. 1 Illustrates how a turbine cycle mey be conflgured with these basic modules. C lose examination of this figure Illustrates that the turbine cycle configuration can regard the turbine as being divided Into Individual stege modu les and the feedwat1r heaters can be regarded as consisting of two heat 'trllnsfer sections, the steam condensing section and the drain cooling . section. Through an InterllCtlve user friendly off-.llne menu driven progrllm utilizing IIn IBM PC based worXstlltlo~ the turbine cycle conflgurlltlon Is defined by specifying the beslc unit modules and their Interconnections viII sireams. The off-.llne program accepts this Input and generlltes a configuration data file for the o~lIne turbine cycle conflgurlltlon. For each of the units specified. the o~lIne program automatically generates the appropriate equations representing material and energy balances together with the parameter specifications. This Informlltlon Is then used with the measured data to compute the Individual performance Indices. StAtIstical Model

• feedwat1r heat1r hellt 'transfer effectiveness for the drain cooling section The boiler efficiency Is det1rmlned by the heat loss method. The losses Included In the analysis consist of the follow Ing. • • • • • • •

unburned carbon heat In the dry fl ue gas moisture In the fuel moisture from burning hydrogen moisture In air formation of cllrbon monoxide surface radllltlon and convection

The turbine efficiency Is computed by the enthelpY"' drop method. The flow coefficient assocla18d with ellCh turb Ine stage Is defined as the retlo of the flow through the turbine stage to Its corresponding pressure drop. lower than expec18d flow coefficients may Indlcete excessive bladlng deposits while e higher value may Indlcllte Increesed leekege or blede erosion. The heet transfer effectiveness of the condenser end feedwater heaters Is defined as the ratio of the lICtual heat 'transfer to the thermodynamlcelly maximum heat transferred, essumlng en Infinite surfllCe are!\. lower than expec18d heat 'transfer effectiveness may Indlcete excessive corrosion deposits or tube leakage. Modylorlty of Components To ensure that the performance assessment pllCkage can be applied to a wide variety of plant configurations, a modular approach to the computetlons has been edop18d. Computations assocle18d with the boiler efficiency are compu18d separate from the turbine cycle. Furthermore, since the turbine cycle consists of several plant components (such as turb Ine stages, condenser and feedwet1r

A statistical approach Is used In all the calculations to assess performance. The statistical approach det1rmlnes the stlltlstlcal ''bes1'' estimate of plant performance Indicators based upon plant measurements and their corresponding acx:uracy. On a typlclll plant both the feedwater flow and the condensate flow are measured. The power test codes recommend choosing the flow meesurement which provides the best computational acx:uracy. With the statistical approach, the user can selectively specify, o~lIne, the flow rate measurement that should be used as the basis for the performance computations, alternately the user can elect to use both meesurements In the analysis. Selecting both measurements permits the statistical approllCh to take advantage of any overspeclflcatlorYredundancy of measurements In the turbine cycle and consequently. to provide a better estimate of the performance Indicators than the typical deterministic clllculatlons. A n Important benefit of the statistical approach Is Its eblllty to compute a confidence Interval for ellCh performance Indicator. For example, calculating a turbIne efficiency of 85% does not have much value by Itself. It Is Important to provide the user with Information on the confIdence that can be plllCed In this value. Not know Ing how acx:urate the computed result Is can lelld to Incorrect conclusions concerning equipment performance and thus, lead to Incorrect deciSions on equipment maintenance and upgrade. With the statistical approllCh, ellCh compu18d performance Indicator Is provided with 8 quantitative IndIcation of the lICcuracy of the results. Thus, providing the user add Itlonal Inform atlon to det1rm Ine whether the change In performllnce Is due to measurement error or actual equIpment degradetlon.

217

Perform ance Assess ment and Statistical Process Cont rol Outpyt Displays Plant assessment Information Is viewed through carefully selected displays. These displays are grouped Into three basIc categories, el!Ch category Is Important for a specifIc objective. 1. The Plant Performance Summary display provides Information of the current operating performance of the boIler, the turbine and the overall plant. This Information Is typIcally 1rended over long periods of time to Identify general operating problems. The results frequently become useful when major plant maintenance or modifIcations have been made. The monitored Increase In plant performance can then be used to help quantify and Justify the expense of upgrades. A typical example of the Plant Performance Summary display Is Illustrated In Fig. 2.

2. Monitoring control lab le parameters takes on significant Importance when their deviation from a desired value can be associated with an Increase In plant operating costs. This provides Information to the operator and thus, an Incentive to adjust the plant operating conditions for maximum efficiency. Fig. 3 Illustrates a typIcal display for the Controllable Parameters. 3. Monitoring Individual component performance provides specifIc Information on the unit's capabIlity. This Is useful for the engineer and plant management. Significant changes In performance over a short period of time may IndIcate equipment prob lems that require Immediate attention. Performance may also be monitored over a longer period of time to determine the rate of deterioration. Informed decisions can then be made on when the next maintenance should be scheduled. Fig. 4 shows a typical display for the turbine performance assessment. Fig. 5 shows a display for the high pressure feedwater heaters. SIm 11 ar dlspl ays for the low pressure feedwater heaters and the condenser are also avail ab le. APPLICATION OF SPC TO OPERATION AND MAINTENANCE COST StatistIcal Process Control (SPC) provides a means to monitor and statistically analyze performance parameters with random variation. SpecifIc SPC tests can be performed to determine whether a performance parameter Is behaving consistently or abnormally. A brief description on the basic principles of SPC as It Is used In on-line monitoring systems Is described In the ap pend Ix. Standard on-line SPC analysis tools are readily available on the INFI 9:)'NETWORK ~ system. On-line SPC control charts are conf lgured Into the operations console, simIlar to other process variable trend displays. The automatic alarming capabilIties of the INFI 9:)'NETWORK ~ system can be Integrated with the SPC tools to force operator acknow ledgment of abnormal process variation. The computation of both the actual and expected performance values from the performance assessment package makes It possible to readily Implement SPC as an on-line detection of equipment abnormalities. For each performance Indicator calculated In the performance assessment package, e corresponding performance Index Is computed as:

performance Index = I!Ctual performance value expected performance value

x 100%

While actual and expected performances vary with load conditions, the performance Index tends to remain statistIcally constent. Thus, the performance Index Is a suItable parameter to monItor on the SPC control charts. FIg. 6 Illustrates a Shewhart Control Chart for turbIne efficIency. In a typical scenarIo, If X-BA R exceeds Its control lImIts, a control lImIt alarm would be InItIated. ThIs would ImmedIately alert the operator of a possib le turbIne malfunctIon, lIkely to be of catastrophic nature due to the abrupt change detected. The operator could then evaluate the situation to determine the appropriate course of action. WhIle catastrophic faIlures can be Identified by other means, the real benefits of using SPC techniques on-line are those whIch Involve detecting small but Important abnormalIties In plant performance. These abnormalIties may not be visually apparent to control room personnel through normal plant monitoring. But with SPC pattern tests, early detection of sm all changes In performance soon enough can avert unscheduled outages and catastrophic damage to equipment. For illustrative purposes, consider the case w here there Is a gradual Increase In turb Ine leakage but not enough to be readily apparent. Depending upon the specific nature and random variation of the 1rend for the performance Index, any or all the follow Ing pattern tests cou Id be activated: Test 1 -Nine consecutive points on one side of XBA R-BA R, Test 2 -Six points In a row steadIly decreasing, Test 5 - F our out of five po i nts In a row In Zone B or beyond. An elerm from test 1 Indicates that something may have been Induced Into the process to generate a small bias Into the performance Indicator. A n A larm from test 2 might Indicate a gradual and continuing degradation of the equipment performance. An alarm Initiated as a result of test 5 might Indicate that a repeatable event Is occurring. WhIle these pattern tests do not In themselves Identify the cause of the prob lem, they do alert pi ent personnel of abnormal behavlor so that appropriate action may be taken. CONCLUSION Instelletlon of the performance assessment package Is easily I!Ccompllshed by adding Multi-Function ProcessoriController modules to a INFI 9:)'NETWORK ~ system. By sharing resources with the distributed control system, such as the data acqu Is Itlon system and the operetor's console with Its trendlng'archlvlng capab 11 Itles, the performance assessment package becomes an econom Ically attractive package. Performance assessment goes beyond traditional performance monitoring by providing on-line comparison between current end expected plant performance. Online performance assessment provides operators, engineers and managers critical Information to maximize plant efficiency and availabIlity.

R. G. Kneile , G. L. Stephens and K. S. Vasudeva

218

The statistical approcch used In the performance computations permits the statistical "besi" estimate of the performance parameter to be computed. The statistical approcch also permits a confidence Interval for ecch performance parameter to be computed, thus providing a quantitative Indication on the cccurccy of the resu Its. Furthermore, the performance assessment pcckage can be used as an Intermediate step towards more advanced monitoring tools such as oo-lIne Statistical Process Control. Using Shew hart C harts as one of several posslb le Statistical Process Control techniques, the detection of Incipient failures, not normally detected by conventional control room Instrumentation, can assist In averting catastrophic damage and unscheduled outages. APPENDIX: BASIC PRINCIPLES OF ON-LINE STATISTICAL PROCESS CONTROL Statistical Process Control (SPC) Is based on the fundamental principle that a process parameter will have variation due to random process changes and measurements. A Iso, this randomness In the process parameter w III have a statistically normal distribution which can be quantified from elementary statistical procedures. Once the randomness In the process parameter Is quantified, statistical tests can be applied to recent data to determine If an abnormal variation has occurred. SPC Is a management philosophy which seeks to quantify and optlmlze process variation. Many of the tools for SPC were provided by the pioneering efforts of Waiter Shewhart and W. Edwards Demlng, which resulted In a group of techniques collectively called Statistical Quality Control. These techniques are solid Iy grou nded In statistical theory and have been widely applied. Many of these techniques are best suited to off-line, retrospective analysis of historical process data. However, some of these techniques can be effectively utilized with live process data to provide oo-lIne support for SPC cctlvltles such as oo-lIne SPC control charts and SPC alarms. These oo-lIne SPC tools collectively alert the operators to abnormal process variation and help to Identify the causes of the abnormal variation. Oo-llne SPC Control Charts I n the context of gathering dat~ an SPC control chart Is a specific type of time trend for a specific process parameter. One of the more popular control charts Is the Shew hart Chart. The Shew hart Chart cctually consists of two parallel trend charts (F Ig. 6). Ecch point on the chart represents data associated with a subgroup of data collected In succession. The number of values In a subgroup Is predeterm Ined, typically 4 or 5. The first chart trends the follow Ing parameters relating to the magnitude of the subgroup. • • • •

average (X-BA R) of the values within the subgroup, grand average (X-BA R-BA R) of all values In the database, upper control IIm It WC L> for X-BA R, lower control limit (LCL> for X-BAR.

Since X-BA R Is expected to approximate a normal distribution, It Is possible to establish a confidence Interval around X-BA R w here the chances of going

outside these limits would be extremely small. The boundaries of this confidence Interval Is referred to as upper control limit WCL> and lower control limit (LCL>. In prcctlce, these control limits are customarily set at a ~7% confidence level, Indicating that X-BA R will fall within these IIm Its cnT times out of a 1000. In a similar manner, the second chart trends parameters relating to the amount of dispersion of the data such as: • range (R) of Individual values within the subgroup, • average range (R-BA R) for all subgroups In the database, • the upper control IImltWCLR) for R. The range R Is computed as the difference between maximum and minimum values In ecch subgroup. Since R Is always a positive number, there Is no appropriate lower control limit for R. The control limits for X-BA R are computed from the grand average X-BA R-BA R of the data collected In the database and the average range of values In the subgroups: UCL

X-BAR-BAR + FX

LCL = X-BA R-BA R - FX

* R-BAR * R-BAR.

FX Is a statistical significance fcctor based on the number of data samples In ecch subgroup. As the number of data samples In ecch subgroup Increases, the value of the statistical significance factor decreases and the control limits move closer to X-BA R-BA R. The upper control limit for R WCLR) Is computed as: UC L R

= FR *

R-BA R

Similar to FX above, FR Is also a statistical significance fcctor based on the number of data samples In ecch subgroup. SPC Alarms The automatic alarming capabilities of INFI becomes a vital part of oo-lIne SPC control charts by alerting the operator Immediately of any abnormal changes. This Is especially true when there are numerous SPC control charts to monitor, abnormal variation could go undetected for hours without oo-lIne SPC alarms. Through the INFI ~NETWORK ~ system, cctlvatlon of an alarm forces ccknow ledgment by the operator In addition to automatically logging Itself. ~NETWORK ~

A n alarm condition occurs w hen either X-BA R or R falls outside of Its respective control IIm Its. Both varlab les are trended due to their strong Interrelationships. If, for example, a significant change In X-BA R occurs with R remaining relatively constant, a change In the performance of the equipment Is likely to have occurred. On the otherhand, a rei atlvely constant X-BA R with a substantial Increase In R suggest something unusual external to the equipment (e.g. noisier measurements) may be upsetting the random ness In X-BA R. Other alarm tests for abnormalities are also possible, even w hen X-BA Rand R stay within their control limits. These type of tests are typically called pattern tests. They are designed to detect abnormalities by examining many data points In sequence to determine If a specific pattern exists.

f

Performance Assessment and Statistical Process Control

21 9

Pllttern tests 111"8 besed upon dIvIdIng the regIon between the control limIts Into three equlllly-speced zones on eech sIde of X-BAR-BA R. •

Zone C 15 the zone closest to X-BA R-BA R,

•

Zone B Is the zone next closest to X-BA R-BA R,

•

Zone A 15 the zone furthest from X-BA R-BA R but closest to UCL and LCL.

While there Is no stllndllrd Ized set of pllttern tests, some of the more common pllttern tests lire show n In FIg. 7 IInd listed below. 1.

NIne poInts In a row In Zone C or beyond.

2.

Six poInts In a row stelldlly Increeslng or decreas Ing.

3.

Fourteen poInts In a row alternlltlng up IInd down.

4.

Two out of three poInts In a row In Zone A or beyond.

5.

Four out of fIve poInts In a row In Zone B or beyond.

6.

FIfteen poInts In below centerllnel.

7.

EIght poInts In a row on both sIdes of centerllne wIth none In Zone C.

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R. G. Kneile. G. L. Stephens and K. S. Vasudeva 14111118 I9-JAH-8!J l'HIJR5DAY

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Performance Assessment and Statistical Process Control

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