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VGB's KISSY to forecast availability VGB PowerTech 10 l 2015

Author

Kurzfassung

Zuverlässigkeitsprognosen von Kraftwerkskomponenten mit Hilfe der VGB-Datenbank KISSY

Qualitativ hochwertige Zuverlässigkeitsdaten für Kraftwerke werden in allen Lebens phasen benötigt und aus diesem Grunde erfasst. Häu-fig existieren Zuverlässigkeitshandbücher für Kernkraftwerke. Für konventionelle Kraftwer-ke gilt dies nicht. Im VGB-Forschungsprojekt „Reliability Indicators with KISSY“ (Projekt-nummer 361) wird eine Methode zur Berech-nung von Zuverlässigkeitskennzahlen auf der Basis des VGB-Kraftwerksinformationssystems KISSY entwickelt. Im Projekt-Abschlussbericht werden die Er-gebnisse sowie deren Verwendung ausführlich beschrieben. Die Ergebnisse klassifizieren die Zuverlässigkeitsindikatoren nach Kompo-nenten in dreistelliger KKS-Auflösung (Kraft-werks-Kennzeichensystem), nach Kraftwerk-styp und nach Anlagenalter. Dabei werden im Bericht Anlagenunterschiede hinsichtlich ihrer Auslegung und Alterung der Bauteile sowie die statistische Verteilung der Reparaturzeiten dargestellt. Ein Beispiel zur Nutzung der Daten verdeutlicht die Anwendung. Ein weiteres Ziel ist es, die Methoden und Zuverlässigkeitskenn-werte in die KISSY-Datenbank, beispielsweise als Online-Berichte, zu integrieren. l

Forecasting the reliability of components in thermal power plants using the VGB database KISSYHenk C. Wels

Henk C. WelsDEKRA Solutions B.V.Material Testing & InspectionAsset Integrity ManagementArnhem, The Netherlands

Why unavailability data?

Power plant failure can be costly and any effort should be made to prevent such failures. Methods using Reliability Analy-sis Maintainability (RAM) tools are well known to answer questions on target values for reliability and availability of a plant, capacity expansion planning, opti-mum redundancy, maintenance optimisa-tion, etc. However, such RAM tools need high-quality reliability data. These data are scarce for power plant components, therefore VGB PowerTech e.V. decided on a research project to arrive at such data from its KISSY database. While the yearly standard VGB reports sufficiently show which systems and components in thermal power plants are prone to unavailability, forecasting the reliability of components is not optimum as the number of components at risk is not taken into account. Therefore, one cannot determine the failure probabil-ity per unit time, the average repair time etc. for sub-systems and components from these reports. Also some components have HILP = High Impact Low Probability char-acteristics and, while the contribution to plant unavailability is in the standard re-port, for such components due to the low probability of occurrence, the frequency of occurrence is not in the reports. The work was carried out with the author being employed at DNV-GL, now at DEKRA. The work was supported and guided by the VGB European Working Group “Perfor-mance Indicators”. The paper highlights the approach to derive RAM data for forecast-ing the forced unavailability. It shows a typi-cal standard report and gives examples of application with respect to ageing and op-timum redundancy in a feedwater system.

The KISSY database

Since 1988 data on the unavailability of power plants have been gathered at VGB. This has grown into a modern computer-ised system containing raw failure informa-tion that, downloaded to Excel, is shown in Table 1. Based on inputs by plant engineers and operators, for each event that causes an outage or power curtailment, the begin and end of the outage/failure is recorded, together with the system that caused the failure in the KKS Kraftwerk-Kennzeichen-System (which is a reference designation

system in power plants). The background of the event is coded with the EMS Ereignis Merkmal Schlüssel system (which is a cod-ing system for incidents in power plants). The comments describing the circumstanc-es of the outage are especially helpful for explanations and further analysis.

The plants in the KISSY database are anonymous to third parties. For the R&D project, plants were grouped into type of plant, fuel, capacity in MW, etc. to make failure data as specific as possible. Also, to assure the quality of data, VGB staff in-cluded only plants that had contributed for several years as well as plants where the summed total plant unavailability in the database part “Availability of Thermal Power Plants” was consistent with the sub-systems/components database “Analysis of Unavailability of Thermal Power Plants”. For the majority of plants analysed in the R&D project, data were available over a 10-year period up to 2011. In total, 200 plants were analysed on failure probability per unit time, repair times, forced unavailabil-ity, repetition of failures, etc. as a function of age, operating hours per year, teething troubles versus ageing, etc. Regrettably for conventional power plants only the aver-age operating hours per year, not the num-ber of starts, was available.

Selection of plants

It was agreed with the members of the VGB European Working Group “Performance Indicators” to select the following plant types in the VGB KISSY data:

– Modern coal- and lignite-fired plants, at an age of up to and including 10 years calendar time at the end of the data pe-riod (2011).

– Mid-life plants, at an age of up to and in-cluding 25 years calendar time at the end of the data period.

– Older coal- and lignite-fired plants, at an age of over 25 years calendar time, being of interest given the present tendency to operate plants as long as technically and economically feasible.

– Combined cycle plants, – Open-cycle gas turbines (heavy duty or

aero-derivative type (“jet engines”)) – Nuclear plants (PWR (pressurised water

reactors) and BWR (boiling water reac-tors)).

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VGB PowerTech 10 l 2015 VGB's KISSY to forecast availability

For the given set of plants, standard layouts were made and send to the data suppliers for confirmation.

Selection of data

From the raw KISSY data, failures were se-lected based on Ereignis Merkmal Schlüs-sel (EMS) codes with overhaul events not included in the analysis as we focused on forced outages. For the same plant and KKS system, the cumulative number of failures together with age of the plant at occur-rence of the failure was used for the analy-sis of ageing. Repeat failures occurring in one week were disregarded in the ageing analysis as this is an indication of insuf-ficient root-cause analysis. However, the fraction of repeat failures can be used by plant owners as an indicator for improving forced unavailability. Finally, plant trips or other unplanned automatic grid separa-tions (known as “UAGS”) were calculated from the EMS code as well as from check-ing for “trip” in the comment field. A differ-entiation between UAGS and failures that can be postponed to period of low demand (at night or during weekend) is especially important for power plants that cycle. Those repair times can be shifted to times of low prices to keep commercial loss low.

Selection of KKS codes

Raw data were supplied for analysis in spreadsheets. As the amount of work in-volved increases linearly with the num-

ber of KKS codes to analyse, we confined ourselves to KKS codes that are dominant with regard to forced unavailability ac-cording to Pareto’s 80-20 rule. For these KKS codes standard reports were made. However, in the underlying spread-sheets for each power plant class one is

able to enter any valid KKS-code on 1, 2 or 3 characters and calculate a standard report with typical RAM parameters such as a failure probability per unit time, aver-age repair time, etc. with either the num-ber of components be derived from each plant layout or assuming only one compo-nent being present if the component is not present in the underlying layout data.In F i g u r e 1 and 2 , the Pareto analysis for modern and for aged coal- and lignite-fired plants are given. Only a few components are causing the majority of forced unavail-able MWh. For both types of plants boiler components such as HAD evaporator, HAH superheater, etc. are dominant with regard to forced unavailability. Regrettably this is known for decades now and apparently it is difficult (but not impossible) to remove these weak points.

Complete data report

An example of a standard data report for HNC (induced flue gas fans) of mid-life coal-fired plants is shown in F i g u r e 3 and 4 . It essentially shows:

– The average failure probability per unit time (the failure rate for reliability en-gineers) and average repair time for all outages taking the number of compo-nents into account and for full outages.

– A numerical and graphical overview of differences between plants including the statistical uncertainty interval for such differences.

– These above parts are straightforward and can be used for standard RAM cal-culations. A special overview shows the

MW

h

600,000

500,000

400,000

300,000

200,000

100,000

0

%

100

90

80

70

60

50

40

30

20

10

0

Sum of unavailable energy in MWhCumulative fraction of unavailable energy

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 332 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34

1: HAD evaporator2: MKA generator stator and rotor3: HAH superheaters4: M main machines5: HAJ reheaters6: HA pressure system7: MAB IP turbine8: H boiler9: LBA main steam piping10: HDA slag removal11: MA steam turbine12: HHB „Nachbrennrost“13: LAC feedwater pumps14: HNA flue gas channels15: LBF HP bypass16: BAT stepup tranformer17: MAA HP turbine

18: HLD air heaters19: HFC coal mills20: HNC induced flue gas fan21: HND „RVS außer Betrieb“22: LCB condensate pumps23: MAC LP turbine24: HTD flue gas deaning25: HN flue gas exhausting26: MAW ST glands & seals27: LBA main steam piping28: LCA condensate pumps29: MAG ST condenser30: LBS extraction stema piping31: LBB hot reheat piping32: HLB forced air ventilators33: L feedwater system34: MAN HP bypass

Fig. 1. Pareto-analysis: modern coal & lignite fired plants.

MW

h

3,500,000

3,000,000

2,500,000

2,000,000

1,500,000

1,000,000

500,000

0

%

90

80

70

60

50

40

30

20

10

0

Sum of unavailable energy in MWhCumulative fraction of unavailable energy

1 3 5 7 9 11 13 15 17 19 21 23 25 272 4 6 8 10 12 14 16 18 20 22 24 26

1: HAD evaporator2: HAH superheaters3: HA pressure system4: HAJ reheaters5: MKA generator stator and rotor6: MAC LP turbine7: MAA HP turbine8: MA steam turbine9: MAD ST bearings10: MKD generator bearing11: MAB IP turbine12: BAT stepup tranformer13: HAC economiser14: MK generator

15: H boiler16: HFC coal mills17: B E-supply18: LB steam supply19: HNC induced flue gas fan20: EC soloid fuel distribution21: LAC feedwater pumps22: MAG ST condenser23: MKG generator gas cooling24: C process computer25: PAC main cooling water26: HLB forced air ventilators27: HDA slag removal

Fig. 2. Pareto-analysis: aged coal & lignite fired plants.

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VGB's KISSY to forecast availability VGB PowerTech 10 l 2015

amount in which the cumulative num-ber of failures is a function of operating time and the cumulative time since first failure together with coefficients for age-ing and the distribution of repair times. This part for the KKS codes analysed has shown that:

– In contrary to expectation, the cumula-tive number of failures does not always increase with more operating time per year. Perhaps this is the influence of starts per year for instance for coal-fired mid-life plants which are nowadays on cycling load.

– The cumulative number of failures sometimes shows typical step functions, which are indicative for not solving root cause problems.

– A log-normal fit for repair times is gen-erally a much better approximation than the usual exponential distribution, which is taken for convenience only as it contains only 1 parameter (the average value).

The complete data reports for all compo-nents that are dominant in the chosen sub-sets of plants are given in the VGB Research Project “Reliability indicators with KISSY”, Project 361 (ISBN: 978-3-86875-751-4) [1]. This is now one of the few publically available reports giving β values to be used for the analysis of ageing, which is impor-tant for Life Time Extension projects and maintenance optimisation.

Analysis of ageing

Due to differences in age of the plants in any database and the number of years that they have been contributing failure data, the data for each plant do not cover the same period. In such a case, the so called Crow-AMSAA model should be used. The typical Weibull model cannot be used as components are not as good as new after repair. Full reference is given to the origi-nal Larry Crow 1990 IEEE symposium pub-lication. The cumulative number of failures is given by:N(t) = λ · t ^ β withλ = parameter for the number of failures

per ht = time (h)β = coefficient to describe ageing (similar

to Weibull β coefficient)With β = 1, no ageing is present and N(t) is approximately a straight line. The coef-ficient λ in that case is equal to the usual failure rate (probability per unit time). With β > 1, N(t) curves upward and ageing appears to be present. With β < 1, the gra-dient of N(t) decreases and teething prob-lems appear to be present. The coefficient λ in these cases, while having the dimension of failure rate, takes on different values that can be orders of magnitude different from the usual failure rate. The value of β is calculated from the data by an iterative

Fig. 3. Standard report for HNC = Induced flue gas fans.

Plant plot no

1 2 3 4 5 6 7 8 9 10

Plant plot no

1 2 3 4 5 6 7 8 9 10

Failu

re ra

tepe

r hou

rAv

erag

e re

pair

time

in h

ours

1.00E-02

1.00E-04

1.00E-07

10,000

1,000

100

10

1

Fig. 4. Graphical presentation for differences between plants for HNC = Induced flue gas.

Cumulative calendar hours

Cum

ulat

ive

num

ber o

f fai

lure

s

6.0

5.0

4.0

3.0

2.0

1.0

0.00 100,000 200,000 300,000 400,000 500,000 600,000

Y = 2E-11x2 + 3E-06x

Fig. 5. Cumulative # of failures for BAT = stepup transformer.

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VGB PowerTech 10 l 2015 VGB's KISSY to forecast availability

procedure. As this procedure not always converges and one power plant with many failures can spoil the pattern, in such cases the coefficient was set to β = 1.

For BAT = stepup transformer, in F i g -u r e 5 the cumulative number of failures is shown as a function of time. Since the line only slightly curves upward, on aver-age there is medium ageing with β = 1.6. Therefore, unless the condition of the transformer is deteriorating indicating im-minent failures, age alone is no ground for replacement.

An alternative presentation is to show for each plant and each age the failure prob-ability per unit time, the average time to re-pair and the average forced unavailability over 10 years. F i g u r e 6 shows for HAD

= evaporator on average that the older a plant, the higher the failure probabil-ity and forced unavailability. This higher forced unavailability is caused by failure rate as the average repair time does not increase with age. There appears to be a hump around 30 years of age. We have in-vestigated this further using the technical failure descriptions which show that, per-haps due to minimal maintenance, the con-

dition is deteriorating. Further analysis of descriptions for other components shows typical problems that may occur.

An example of plant modelli ng

With input using mid-life coal- and lignite-fired plants KISSY data, a plant model was made using the RAMP software for Reli-ability Block Diagrams (RBD) of Atkins. Other Reliability Block Diagram software is commercially available. RAMP uses Monte Carlo simulation to calculate the average value, number of outages and equivalent forced unavailability. Now, any power plant can be modelled as a system of series- and parallel components. F i g u r e 7 shows the sub-system describing part of the feedwater and steam system. Both full outages and partial outages are modelled. For instance the feedwater pump system, node no. 51 M, is simply modelled as a series system for full outages (node no. 58 LAC f) and a parallel system for partial outages (nodes no. 60 and no. 61). Node no. 61 is a dummy component that cannot fail. In terms of plant hardware, node no. 60 is a model for the turbine feedwater pump plus its turbine driver. If they fail, the plant (given the capacity of the electro feedwater pump(s) is large enough so that it will not trip) will have a partial outage. If both the turbine feedwater pump and the electro feedwater pump fail, this is a full outage. In RAMP capacities are described by a Q-value for each component. Please note that due to the so-called common cause fraction, the forced unavailability is larger than that generally calculated using independent failures.The MTBF (1/failure rate – “Mean Time Be-tween Failures”) and the lognormal 50 % and 95 % value repair time for the full out-age description were taken from the KISSY data, taking utility feedback on plants hav-ing turbine feedwater pumps into account. The capacity of the electro feedwater pump was assumed to be 70 %. F i g u r e 8 shows the distribution of total plant Q values (availability taking only forced outages into account) when simu-

Age in years

Failu

re ra

te/h

our

1.2E-03

1.0E-03

8.0E-04

6.0E-04

4.0E-04

2.0E-04

0.0E+000 10 20 30 40 50 60

Age in years0 10 20 30 40 50 60

Age in years0 10 20 30 40 50 60

Aver

age

repa

ir tim

e in

???

????

200180160140120100

80604020

0

Forc

ed u

nava

ilabl

ity in

%

6

5

4

3

2

1

0

Modern plant Mid-life plant Aged plant Average

Fig. 6. Ageing of HAD = evaporator.

Fig. 7. Part of the RBD for a feedwater and steam system.

Mid-life coal fired plant, RAMP calculations

Equivalent forced unavailable in %

Prob

abili

ty o

f occ

uren

ce in

%

100

80

60

40

20

00.0 5.0 10.0 15.0 20.0 25.0

Fig. 8. Cumulative distribution of unavailability over a period of 10 years.

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VGB's KISSY to forecast availability VGB PowerTech 10 l 2015

lating 5,000 times over a period of 10 years of (base load) operation. The figure shows that the distribution is skewed, therefore the results with low availability will sub-stantially influence the average. The equiv-alent forced unavailability is 3.5  %. The cumulative frequency curve is easily in-terpretable as it shows that there is a 90 % probability that the forced unavailability is worse than 2.8 %. Similarly, there is a 10 % probability that (averaged over a period of 10 years) the forced unavailability is worse than 5 %. Any plant that has an equivalent forced unavailability of 10 % over 10 years almost certainly performs worse than av-erage. Please note that the results are de-pendent on the period over which they are averaged. For instance over 1 year there is a 90 % probability that the forced una-vailability is worse than 1.6 % and a 10 % probability that the forced unavailability is worse than 6.2 %. The shorter the period, the larger the deviations from the average that can be contributed to chance. Teething problems were not taken into account.

A typical example of the use of RBD’s is to answer on the value of larger redundancy:

“What would be the improvement in una-vailability, if the capacity of the electromo-tor feedwater pumps is raised such that with a failure of the turbine feedwater pump full production is still possible?”

When the fraction common cause failures does not change and all partial outages are remedied, the simulations now indicate an average forced unavailability of 3.26  %. The added redundancy results in (3.5  % to 3.26  %) · 8760 = 20.7 hours per year equivalent full production. By multiplica-tion of the MWh value of production and application of a Net Present Value Method over the remaining life of the plant, one can calculate the NPV (Net Present Value) of the additional redundancy in the feed-water system.

Typical questions to answer by ram analysis with high-quality data

Questions that can be solved by data analy-sis and RAM simulation are:

– The unavailability of a plant is regis-tered. However, is an unavailability of 5 % to 10 % a good value or a reasonable value given the layout of the plant or should a project be started for improve-ment? Which forced unavailability is fea-sible? How long should measurements be taken before they can be considered as reliable?

– Something happens on a steam turbine causing a 1 month outage. Is this high impact failure an incident (a low prob-

ability event) or is the frequency of such incidents at this plant out of bounds? Which components are generally causing HILP failures? Does the probability of an HILP increase with age?

– What is the amount of teething problems that can be expected for a new plant? Which components will cause teething problems? What average value and what range of forced unavailability can one expect? To what extend should redun-dancy be installed?

– As an alternative to a new build plant one considers extending the life of the old plant. Which components are likely to suffer from ageing (increase in failure frequency) and maintainability prob-lems (increase in repair time)?

– Spare parts analysis to weigh the costs for spare parts against the financial ben-efits of shorter repair times. Evidently the distribution of repair times is impor-tant to solve this question.

Questions that need expert judgement in addition to data analysis and RAM simula-tion are:

– Changing the way of operation of a plant. In what way does the forced una-vailability change for the good or worse? In addition to failure data, this requires a good engineering understanding of the failure mechanisms involved and

Tab. 1. Raw KISSY information.

Unit name

Begin/ date

Begin/ time

End/ date

End/ time

Capacity power plant

Unavailable capacity power

plant (%)

Unavailable energy (MWh)

KKS EMS1 code

EMS41 code

EMS42 code

Comment Type of power plant

27 29-08-2008

21:23 16-09-2008

7:33 5: 600 to 999 MW

96.15 365985.834 999 B7 – inspection

K – annual autage pro-

gramme

A – standstill

Inspection Fossil unit mono

27 17-01-2003

19:09 18-01-2009

2:16 5: 600 to 999 MW

95.05 6155.916 BAB AZ – damage

A – auto-matic load shedding/tripping

A – standstill

Failure caused

by broken trans-

former in generator protection

Fossil unit mono

27 17-01-2011

2:17 17-04-2001

3:41 5: 600 to 999 MW

96.15 1225.001 BAT B5 – pre-ventive mainte-nance

A – stand-still

2 – power restriction

OM oil sample machine

transformer

Fossil unit mono

27 28-06-2002

2:45 28-06-2002

8:29 5: 600 to 999 MW

73.08 3912.666 BBB A1 – failure without damage

A – auto-matic load shedding/tripping

2 – power restriction

Ground fault

influced flue gas fan

Fossil unit mono

27 13-07-2010

18:07 14-07-2010

2:04 5: 600 to 999 MW

96.15 8956.251 BFB A1 – failure without damage

A – auto-matic load shedding/tripping

A – standstill

Failure switch-gear

Fossil unit mono

27 13-11-2011

14:28 13-11-2011

16:11 5: 600 to 999 MW

96.15 1502.084 BFB A1 – failure without damage

A – auto-matic load shedding/tripping

A – standstill

Failure feed-switch

BPB

Fossil unit mono

27 06-05-2003

5:42 06-05-2003

7:13 5: 600 to 999 MW

95.05 1311.916 CJA A1 – failure without damage

A – auto-matic load shedding/tripping

A – standstill

Failure steam cycle (enthalpy controler)

Fossil unit mono

27 06-07-2005

17:23 06-07-2005

17:41 5: 600 to 999 MW

72.53 198.001 CJA A1 – failure without damage

A – auto-matic load shedding/tripping

A – standstill

Failure after event in 2nd mill

Fossil unit mono

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VGB PowerTech 10 l 2015 VGB's KISSY to forecast availability

the way in which these mechanisms are depending on starts, stops, operating hours and hours with the component conserved (or not).

– Maintenance optimisation. This requires further development of ageing models; however, KISSY data are gathered at a system level for those events where plant unavailability has occurred. Main-tenance optimisation is carried out at a component level, at present by using bathtub curves showing the point in

time where maintenance on a specific component is optimum. While many maintenance optimisations assume that a component is as-good-as-new after maintenance, trending from KISSY data shows that this is seldom the case. The KISSY data show at least those systems and components where maintenance is not removing the failures to the extent possible.

We hope that the data derived will contrib-ute to an optimum plant availability based

on sound decision making by taking these data into account.

References[1] VGB-TW 104: Reliability Indicators with

KISSY − VGB Research Project 361. VGB Power Tech e.V., Essen, 2014, ISBN: 978-3-86875-751-4 l

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Con gress Is sue

Vo lu me 89/2009 · ISSN 1435-3199

K 43600

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Focus: Furnaces, Steam Generators and Steam TurbinesUSC 700 °C Power Technology

Ultra-low NOx Combustion

Replacement Strategy of a Superheater StageEconomic Post-combustion Carbon Capture Processes

In ter na tio nal Jour nalfor Elec tri ci ty and Heat Ge ne ra ti onPub li ca ti on ofVGB Po wer Tech e.V.www.vgb.org

Vo lu me 90/2010 · ISSN 1435-3199

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Fo cus: Pro Quality

The Pro-quality

Approach

Quality in the

Construction

of New Power Plants

Quality Monitoring of

Steam Turbine Sets

Supply of Technical

Documentations

In ter na tio nal Jour nal

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00634 K

9913-5341 NSSI · 5002/58 emulo

International Edition

Schwerpunktthema:

Erneuerbare Energien

Hydrogen Pathways

and Scenarios

Kopswerk II –

Prevailing Conditions

and Design

Arklow Bank

Offshore Wind Park

The EU-Water

Framework Directive

International Journal

for Electricity and Heat Generation

Publication of

VGB PowerTech e.V.

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Vo lu me 89/2009 · ISSN 1435-3199

K 43600

In ter na tio nal Edi ti on

Focus: Maintenance

of Power Plants

Concepts of

IGCC Power Plants

Assessment of

Generators for

Wind Power Plants

Technical Data for

Power Plants

Oxidation Properties

of Turbine Oils

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