Boiler OH Intervals-EPRI

Examples Manual for Boiler Overhaul

Examples Manual for Boiler Overhaul

Interval Optimization

Interval Optimization

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EPRI Project Manager EPRI Project Manager R. Tilley

DISCLAIMER OF WARRANTIES AND LIMITATION OF LIABILITIES

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Electric Power Research Institute and EPRI are registered service marks of the Electric Power Research Institute, Inc. EPRI. ELECTRIFY THE WORLD is a service mark of the Electric Power Research Institute, Inc.

Principal Investigators D. Mauney

D. Rosario

This report describes research sponsored by EPRI.

The report is a corporate document that should be cited in the literature in the following manner:  Examples Manual for Boiler Overhaul Interval Optimization, EPRI, Palo Alto, CA: 2004.

overhaul for maximum corporate benefit. In each case, the screen shots illustrating the process steps are shown so that readers can actually use this report as a guide to conduct their own

analysis while referring to User Manual for Boiler Overhaul Interval Optimization on the Fossil  Boiler Overhaul Interval Optimization, Level 1 CD (EPRI Product 1004063). In the final section of this report, an actual case is presented in which a power plant operator started with a specific component in mind and applied the Boiler OIO analysis to set the timing of the overhaul of the component as well as show its value.

Results and Findings

This report demonstrates how the EPRI Boiler OIO process produces a maintenance business  plan for an overhaul of a specific unit. This business plan shows the components that the

overhaul needs to contain, the timing of the overhaul, the value to the power company of the overhaul, and the decrease in value if the overhaul timing is adjusted to other than optimal. In addition, the report guides the reader through how the components that drive the overhaul timing are determined. The examples show that Boiler OIO can provide guidance to producing a net  present value savings of $9–123 million, depending on unit capacity, projected use, replacement

energy value, and component probability of failure.

Challenges and Objectives

This report, written for maintenance and reliability engineers at the corporate and plant levels,  provides a process for timing boiler overhauls for the maximum benefit of the company bottom

line by optimizing net present value savings of the maintenance investment, while being constrained by maintenance budgets. Where applicable, the timing for components of safety concern is flagged. Any engineer or engineering manager who wants to present his or her engineering case to a more financially oriented management will benefit from the Boiler OIO  process. This report presents examples of a futuristic approach to determine what  maintenance

actions should be implemented and when to make a positive contribution to the company bottom line.

vi vi

erodes margins. This effect will be even

erodes margins. This effect will be even more prominent with increased deregulation, whenmore prominent with increased deregulation, when forced outage cost becomes lost-opportunity cost, which can no longer be passed to the rate forced outage cost becomes lost-opportunity cost, which can no longer be passed to the rate  payer through the fuel clause.

 payer through the fuel clause.

EPRI Perspective EPRI Perspective

This technology represents an approach of planning major maintenance that bridges the gap This technology represents an approach of planning major maintenance that bridges the gap  between engineering and finance.

 between engineering and finance.

Approach Approach

The goal of this report is to

The goal of this report is to compile examples that illustrate the broad application of the compile examples that illustrate the broad application of the BoilerBoiler OIO suite of tools. The report encompasses tackling a large system all the way down to starting OIO suite of tools. The report encompasses tackling a large system all the way down to starting with a specific component of concern. The examples are intended to provide a guide of how the with a specific component of concern. The examples are intended to provide a guide of how the suite of tools is applied to attack the scope of each problem. In addition, a description of the suite of tools is applied to attack the scope of each problem. In addition, a description of the component drivers for each outage planned and how these drivers were determined is included. component drivers for each outage planned and how these drivers were determined is included.

Keywords Keywords Boiler Boiler Overhaul Overhaul Economics Economics Decision analysis Decision analysis Maintenance planning Maintenance planning

ABSTRACT

ABSTRACT

This report illustrates the application of the Boiler Overhaul Interval Optimization (Boiler OIO) This report illustrates the application of the Boiler Overhaul Interval Optimization (Boiler OIO)  process on data from four actual power companies. The focus of the report is to illustrate that the  process on data from four actual power companies. The focus of the report is to illustrate that the same process can be applied to a large or small power system, a plant, an individual unit, or even same process can be applied to a large or small power system, a plant, an individual unit, or even an individual component when needed. It illustrates the development of a financial business plan an individual component when needed. It illustrates the development of a financial business plan for the boiler overhaul that includes not only what the overhaul needs to contain but the optimal for the boiler overhaul that includes not only what the overhaul needs to contain but the optimal financial time for the overhaul to occur and its value to the company.

financial time for the overhaul to occur and its value to the company.

The report is written so that after reading Sections 1 and 2, the reader can refer directly to the The report is written so that after reading Sections 1 and 2, the reader can refer directly to the section that most closely refers to the reader’s concern

section that most closely refers to the reader’s concern and receive the same aand receive the same amount of detailmount of detail from the example.

ACKNOWLEDGMENTS

EPRI appreciates the time and effort of staff at Dairyland Power, Salt River Project, and others for sharing the example situations and providing data. Such efforts make this manual realistic in displaying the value of the Boiler OIO process, as well as the steps to apply it.

The risk-based screening and probability-determination spreadsheets are part of the ASME Risk- Based Methods for Equipment Life Management: An Application Handbook, CRTD Vol-41,

2003, ASME, New York, NY, and are used in this report with permission.

The permission to use the Bayesian combination spreadsheet was granted by Southwest Research Institute.

CONTENTS

1 INTRODUCTION ...1-1 Objective of This Report...1-2 2 SYSTEMATIC RISK ANALYSIS PROCESS ...2-1 3 SYSTEM EXAMPLES ...3-1

Small System Example ...3-1 Analysis Process ...3-1 Gather ...3-1 Process...3-2 Risk Rank and Risk Plot ...3-3 Select ...3-6 Estimate...3-6 Input and Optimize...3-11 Examine ...3-13 Conclusion...3-14 Large System Example ...3-15 Analysis Process ...3-15 Gather ...3-15 Process...3-15 Risk Rank and Risk Plot ...3-17 Select ...3-19 Estimate...3-20 For Unit R1...3-20 For Unit W1 ...3-22 Input and Optimize...3-25

 EPRI Licensed Material xii Examine ...3-29 For Unit R1...3-29 For Unit W1 ...3-30 Conclusion...3-31 4 PLANT EXAMPLE...4-1

Large Plant Example ...4-1 Analysis Process ...4-1 Gather ...4-1 Process...4-2 Risk Rank and Risk Plot ...4-2 Select ...4-5 Estimate...4-6 Input and Optimize...4-9 Examine ...4-11 Conclusion...4-12 5 UNIT EXAMPLE ...5-1 Unit Example ...5-1 Analysis Process ...5-1 Gather ...5-1 Process...5-2 Risk Rank and Risk Plot ...5-2 Select ...5-5 Estimate...5-6 Input and Optimize...5-8 Examine ...5-12 Conclusion...5-12 6 COMPONENT EXAMPLE ...6-1 Fan Example ...6-1 Analysis Process ...6-1 Gather ...6-1 Estimate...6-2 Input and Optimize...6-4

LIST OF FIGURES

Figure 3-1 A Portion of the System NERC-GADS Submissions Forced Outage Event Data for the Small System (ET – Event Type, MDC – Maximum Dependable

Capacity) ...3-2 Figure 3-2 Processed NERC-GADS Data for Each Forced Outage Event on the Left, and

the Consolidated Data for Each Plant/Unit/Cause Code by Year on the Right for the

Small System ...3-3 Figure 3-3 Small System Input Data in the “Raw Data” Tab in the Risk-Rank Workbook

Is on the Left, and the Plant/Unit/Cause Code for All Years Is Shown on the Right ...3-3 Figure 3-4 Risk-Ranked System Data for the Small System ...3-4 Figure 3-5 Risk-Ranked Data Inserted Into Risk-Plot Workbook ...3-4 Figure 3-6 Log-Log Risk Plot of Small System Forced Outage Data by

Plant/Unit/Component Cause Code With the Line of Constant Risk Just to the Left

of the 27 Risk-Critical Points ...3-5 Figure 3-7 Diminishing-Risk Plot for Small System Showing up to the 27th Ranked

Component as the Highest Contributors to Incremental Cumulative Risk ...3-5 Figure 3-8 The Small System Risk-Critical Components Sorted by Plant/Unit/Component

Cause Code ...3-6 Figure 3-9 ProbCalc Workbook Calculating the Change in Probability by Year for Cause

Code 9630, Opacity ...3-7 Figure 3-10 Input Worksheet in Baycom11, “Fit of History” Tab, Where the Weibull

Shape (Alpha) and Scale (Beta) Parameters Are Calculated for Input Into the Boiler

OIO for Cause Code 9630, Opacity ...3-8 Figure 3-11 The Cumulative Probability-of-Failure Plot in Baycom11 That Compares the

Weibull Fitted Curve to a Curve Linking the Failure History Points for Cause Code

9630, Opacity ...3-8 Figure 3-12 Results From the Probabilistic Opinion Interview for the Projected Probability

of Failure for Unit D1 Furnace Wall ...3-9 Figure 3-13 Input Worksheet In Baycom11, “Fit of Interview” Tab, Where the Weibull

Shape (Alpha) and Scale (Beta) Parameters Are Calculated for Input Into the Boiler

OIO for Cause Code 1000, Furnace Wall ...3-9 Figure 3-14 The Cumulative Probability-of-Failure Plot “Without Overhaul” in Baycom11

That Compares the Weibull Fitted Curve to a Curve Linking the Interview Points for

Cause Code 1000, Furnace Wall ...3-10 Figure 3-15 The Resulting Fitted Probability-of-Failure “Without Overhaul” Curves for the

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Figure 3-17 Boiler OIO Input “Without Overhaul” for Unit D1 ...3-12 Figure 3-18 Boiler OIO Summary Worksheet for Unit D1 ...3-12 Figure 3-19 Boiler OIO NPV Versus Overhaul Year Results for Unit D1...3-13 Figure 3-20 Boiler OIO Cost to Overhaul Worksheet Showing the Annual Consequential

Cost for the 10 Components Selected for Unit D1 ...3-14 Figure 3-21 A Portion of the System NERC-GADS Submissions Forced Outage Event

Data for the Large System ...3-15 Figure 3-22 Processed NERC-GADS Data for Each Forced Outage Event on the Left

and the Consolidated Data for Each Plant/Unit/Cause Code by Year on the Right

for the Large System...3-16 Figure 3-23 Large System Input Data in the “Raw Data” Tab in the Risk-Rank Workbook

Is on the Left, and the Plant/Unit/Cause Code for All Years Is Shown on the Right ...3-17 Figure 3-24 Risk-Ranked System Data for the Large System ...3-17 Figure 3-25 Risk-Ranked Data Inserted Into Risk-Plot Workbook ...3-18 Figure 3-26 Log-Log Risk Plot of Large System Forced Outage Data by

Plant/Unit/Component Cause Code ...3-18 Figure 3-27 Diminishing-Risk Plot for Large System Showing up to the 25th Ranked

Component as the Highest Contributors to Incremental Cumulative Risk ...3-19 Figure 3-28 The Large System Risk-Critical Components Sorted by

Plant/Unit/Component Cause Code ...3-20 Figure 3-29 ProbCalc Workbook Calculating the Change in Probability by Year for

Cause Code 1000, Furnace Wall ...3-21 Figure 3-30 Input Worksheet in Baycom11, “Fit of History” Tab, Where the Weibull

Shape and Scale Parameters Are Calculated for Input Into the Boiler OIO for Cause

Code 1000, Furnace Wall ...3-21 Figure 3-31 The Cumulative Probability-of-Failure Plot in Baycom11 That Compares the

Weibull Fitted Curve to a Curve Linking the Failure History Points for Cause Code

1000, Furnace Wall ...3-22 Figure 3-32 The Resulting Fitted Probability-of-Failure Curves for the Two Risk-Critical

Components...3-22 Figure 3-33 ProbCalc Workbook Calculating the Change in Probability by Year for

Cause Code 1040, First Superheater ...3-23 Figure 3-34 Input Worksheet in Baycom11, “Fit of History” Tab, Where the Weibull

Shape and Scale Parameters Are Calculated for Input Into the Boiler OIO for Cause

Code 1040, First Superheater...3-23 Figure 3-35 The Cumulative Probability-of-Failure Plot in Baycom11 That Compares the

Weibull Fitted Curve to a Curve Linking the Failure History Points for Cause Code

1040, First Superheater ...3-24 Figure 3-36 The Resulting Fitted Probability-of-Failure Curves for the Two Risk-Critical

Components...3-24 Figure 3-37 Boiler OIO Input “With Overhaul” for Unit R1 ...3-25 Figure 3-38 Boiler OIO Input “Without Overhaul” for Unit R1 ...3-25 Figure 3-39 Boiler OIO Summary Worksheet for Unit R1 ...3-26

Figure 3-44 Boiler OIO NPV Versus Overhaul Year Results for Unit W1 ...3-29 Figure 3-45 Boiler OIO Cost “Without Overhaul” Worksheet Showing the Annual

Consequential Cost for the Two Components Selected for Unit R1 ...3-30 Figure 3-46 Boiler OIO Cost “Without Overhaul” Worksheet Showing the Annual

Consequential Cost for the Two Components Selected for Unit W1...3-30 Figure 4-1 A Portion of the System NERC-GADS Submissions Forced Outage Event

Data for the Large Plant ...4-1 Figure 4-2 Processed NERC-GADS Data for Each Forced Outage Event on the Left and

the Consolidated Data for Each Unit/Cause Code by Year on the Right for the

Large Plant...4-2 Figure 4-3 Large Plant Input Data in the “Raw Data” Tab in the Risk-Rank Workbook Is

on the Left, and the Unit/Cause Code for All Years Is Shown on the Right ...4-3 Figure 4-4 Risk-Ranked System Data for the Large Plant ...4-3 Figure 4-5 Risk-Ranked Data Inserted Into Risk-Plot Workbook ...4-4 Figure 4-6 Log-Log Risk Plot of Large Plant Forced Outage Data by Unit/Component

Cause Code ...4-4 Figure 4-7 Diminishing-Risk Plot for Large Plant Showing up to the 21 Ranked

Components as the Highest Contributors to Incremental Cumulative Risk...4-5 Figure 4-8 The Large Plant Risk-Critical Components Sorted by Unit/Component Cause

Code...4-6 Figure 4-9 ProbCalc Workbook Calculating the Change in Probability by Year for Cause

Code 1000, Furnace Wall ...4-7 Figure 4-10 Input Worksheet in Baycom11, “Fit of History” Tab, Where the Weibull

Shape and Scale Parameters Are Calculated for Input Into the Boiler OIO for Cause

Code 1000, Furnace Wall ...4-7 Figure 4-11 The Cumulative Probability-of-Failure Plot in Baycom11 That Compares the

Weibull Fitted Curve to a Curve Linking the Failure History Points for Cause Code

1000, Furnace Wall ...4-8 Figure 4-12 The Resulting Fitted Probability-of-Failure “Without Overhaul” Curves for the

Six Risk-Critical Components That Remained After Examination of the Applicability

of Including Cause Codes in the Boiler OIO Analysis ...4-8 Figure 4-13 Boiler OIO Input “With Overhaul” for Unit R3 ...4-9 Figure 4-14 Boiler OIO Input “Without Overhaul” for Unit R3 ...4-9 Figure 4-15 Boiler OIO Summary Worksheet for Unit R3 ...4-10 Figure 4-16 Boiler OIO NPV Versus Overhaul Year Results for Unit R3...4-11 Figure 4-17 Boiler OIO Cost “With Overhaul” Worksheet Showing the Annual

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Figure 5-2 Processed NERC-GADS Data for Each Forced Outage Event on the Left and

the Consolidated Data for Each Cause Code by Year on the Right for Unit AA1 ...5-2 Figure 5-3 Unit AA1 Input Data in the “Raw Data” Tab in the Risk-Rank Workbook Is on

the Left, and the Cause Code for All Years Is Shown on the Right ...5-3 Figure 5-4 Risk-Ranked System Data for Unit AA1 ...5-3 Figure 5-5 Risk-Ranked Data Inserted Into Risk-Plot Workbook ...5-4 Figure 5-6 Log-Log Risk Plot of Unit AA1 Forced Outage Data by Component Cause

Code...5-4 Figure 5-7 Diminishing-Risk Plot for Unit AA1 Showing Up to the 10th Ranked

Component as the Highest Contributors to Incremental Cumulative Risk ...5-5 Figure 5-8 Unit AA1 Risk-Critical Components Sorted by Risk...5-6 Figure 5-9 ProbCalc Workbook Calculating the Change in Probability by Year for Cause

Code 3110, Condenser Tube Leaks ...5-6 Figure 5-10 Input Worksheet in Baycom11, “Fit of History” Tab, Where the Weibull

Shape and Scale Parameters Are Calculated for Input Into the Boiler OIO for Cause

Code 3110, Condenser Tube Leaks ...5-7 Figure 5-11 The Cumulative Probability-of-Failure Plot in Baycom11 That Compares the

Weibull Fitted Curve to a Curve Linking the Failure History Points for Cause Code

3110, Condenser Tube Leaks...5-7 Figure 5-12 The Resulting Fitted Probability-of-Failure Curves for the 10 Risk-Critical

Components...5-8 Figure 5-13 Boiler OIO Input “With Overhaul” for Unit AA1 ...5-9 Figure 5-14 Boiler OIO Input “With Overhaul” for Heat Rate Change for Unit AA1 ...5-9 Figure 5-15 Boiler OIO Input “Without Overhaul” for Unit AA1 ...5-10 Figure 5-16 Boiler OIO Input “Without Overhaul” for Heat-Rate Change for Unit AA1 ...5-10 Figure 5-17 Boiler OIO Summary Worksheet for Unit AA1 ...5-11 Figure 5-18 Boiler OIO NPV Versus Overhaul Year Results for Unit AA1...5-11 Figure 5-19 Boiler OIO Cost “With Overhaul” Worksheet Showing the Annual

Consequential Cost for the 10 Components Selected for Unit AA1 ...5-12 Figure 6-1 Results of Probabilistic Opinion Interviews With the Engineer Using the

STACKER Program ...6-2 Figure 6-2 Hand-Fit Weibull Curve to the Run or “Without Overhaul” Probabilistic

Interview Data for the Fan ...6-3 Figure 6-3 Hand-Fitted Weibull Curve to the Interview for the Fan Run “As Is” Case ...6-3 Figure 6-4 Hand-Fitted Weibull Curve to the Interview for the Fan Run “After Repair”

Case...6-4 Figure 6-5 Hand-Fitted Weibull Curve to the Interview for the Fan Run “After

Replacement” Case ...6-4 Figure 6-6 Boiler OIO NPV Versus Overhaul Year Results for the Repair of the Fan ...6-5 Figure 6-7 Boiler OIO NPV Versus Overhaul Year Results for the Replacement of the

LIST OF TABLES

Table 3-1 Table of Component Cause Codes Eliminated From Overhaul Planning for

Conventional fossil boilers have been overhauled for maintenance using a fixed time interval  based on recommendations from the equipment manufacturer or on the interval set for other

major equipment such as the turbine. As boilers aged, the timing for overhauls became different for each specific unit based on how it was operated (for example, cyclic duty versus base load). This means that boilers and the components they comprise need to be treated individually. Consequently, it has been found that maintenance performed on a piece of equipment without regard to the condition of the equipment results in wasted resources in the case where equipment is not aging rapidly or does not impact operations. At the other extreme, maintenance performed on a piece of equipment without regard to the condition of the equipment can result in potentially large financial consequences when equipment is aging more rapidly than expected. This situation  brought about the need for reliability-based and condition-based maintenance to better match

equipment failure consequences with the cost of maintenance to avoid failures. These approaches work well, especially in the cases where the boilers are base loaded and are operating in the first 75% of their expected life cycle.

These maintenance-planning methods are all based on engineering considerations. The boiler has the function of producing power that meets sales demand. Therefore, the methods of

maintenance planning need to have a direct connection between the engineering side and the value-added distribution of maintenance resources for maintenance overhauls in which

equipment can be repaired or replaced. The area of risk-based maintenance planning is being developed to meet this need. The method combines the probability of a shutdown that would  prevent meeting demand (such as equipment failure causing a forced outage of the unit), the

financial consequences of the shutdown, and the overhaul cost to prevent it. This risk-based technology approach is the basis of Boiler Overhaul Interval Optimization (Boiler OIO). The need for a fossil boiler overhaul-planning tool was brought about by several factors that developed concurrently. As deregulation approached the electric utility industry and more financially oriented managers took the positions as decision makers in the utilities, the pressure was significant to extend the period between the usual major boiler maintenance overhaul intervals. If the boiler equipment is in deteriorated condition, extending overhaul intervals can lead to a significant rise in unit forced outages due to equipment failures. This situation

especially applies to aged boilers. An engineer communicating this situation directly to a financially oriented decision maker, who has little or no engineering background, may not be able to convey the appropriate tradeoffs between reduced costs from reduced maintenance versus increased costs due increased risks of equipment failure and associated forced outages.

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 Introduction

1-2

The need, therefore, arose for the boiler engineer to present the case for the next overhaul timing recommendation with full consideration of financial methods based on engineering input. In addition, it was realized that based on boiler conditions, some units could reliably extend their overhaul interval from that being currently used. In short, the boiler engineer needed a tool that would take his or her technical information, unit production need, and economic considerations to produce a rigorous, financially optimized overhaul timing recommendation that management could use for making decisions.

Such a tool would need to show how the proposed overhaul would contribute to the corporate  bottom line as well as be fully justified, as with any other investment opportunity that is

competing for corporate resources. The significance of this capability would be even larger when lost generation cost changes from being based on replacement energy values in the regulated environment to the lost opportunity cost in the deregulated environment. Additionally, the boiler engineer would be able to screen out those components of the boiler that are less significant for a specific boiler with a less resource-intensive analysis. This is so that the components that are really driving the need for the overhaul could have the planning resources concentrated on them. These are the needs that Boiler OIO was designed to meet.

Objective of This Report

EPRI product number 1006716 , User Manual for Boiler Overhaul Interval Optimization, was  published in December 2001. The report describes the operation of the Boiler OIO suite of

Microsoft Excel™ workbook tools. The purpose of this report is the compilation of several examples that illustrate the broad application of the Boiler OIO suite of tools. The examples in this report are drivers, including a whole power system, with a large and small power system examples; a multiple-unit power plant; a single power unit; and a specific unit component example of concern. The examples are intended to provide a guide of how the suite of tools is applied to attack the scope of each problem. In addition, there is a description of the component drivers for each outage planned and how these drivers were determined. This report begins with the most challenging of problems of focusing maintenance resources on a large power system.

At first glance, it does not seem feasible that one systematic process can be used to perform a risk analysis while focusing analytical resources, which can cover the scope from a whole power system to a specific unit. The objective of this report is to take the reader through these

systematic steps, illustrating the consistent method of attack on the task regardless of the initial scope. The systematic steps that will be used repeatedly are:

1. Gather system NERC-GADS (North American Electric Reliability Council-Generating Availability Database System) forced outage event submissions for the last five or six years. 2. Process the data to a specific column format for use in the Boiler OIO suite of tools.

3. Risk-rank and risk-plot forced outage data.

4. Select the risk-critical unit or units and the associated components.

5. Estimate probability-of-failure versus time curves for the selected components. 6. Gather operational and financial information for the unit.

7. Input these data to the Boiler OIO software tool and run the optimization.

8. Examine the results to interpret which components are driving the overhaul timing.

Perhaps on the surface, the most overwhelming task would seem to be the planning of an

overhaul of a unit or units on the whole power system. Even more overwhelming may seem the task of planning with only the information that we have available now. The key to any large task is to break it down into systematic steps with the end in mind that we will have value-added timing on the most critical unit or units on the power system.

Small System Example

To begin our illustrations, we will use a relatively small power system with four units. The risk-analysis steps will be used to determine which unit is the most risk-critical to the system and which components in that unit are creating that criticality. We will determine when the overhaul needs to be and which components need to be overhauled to mitigate this risk and produce the highest net value to the system.

Analysis Process

Gather

A download of the last six years for NERC-GADS submissions was performed as indicated in Figure 3-1. There is one line for each forced outage or derate event. Six years were selected by the power company because they were all that were available and could minimize the number of components that have already been replaced so that minimal effort would be expended in

determining whether already-replaced components are still in the group of those selected as risk-critical.

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System Examples

3-2

Figure 3-1

A Portion of the System NERC-GADS Submissions Forced Outage Event Data for the Small System (ET – Event Type, MDC – Maximum Dependable Capacity)

Process

The raw data were sorted by event type to ensure that only U1-U3 and D1-D3 outages1 were

included. They were then sorted by equivalent hours in ascending order, and all rows with zero equivalent hours were deleted. All columns and rows were sorted by “Unit/Cause Code” and “Year” in ascending order. Then only the columns headed “Unit/Cause Code,” “Year,” “Net Minimum Capacity,” and “Equivalent Hours” were into the left side of a new worksheet, as shown in Figure 3-2.

1

 U1 – Unplanned (Forced) Outage – Immediate U2 – Unplanned (Forced) Outage – Delayed U3 – Unplanned (Forced) Outage – Postponed D1 – Unplanned (Forced) Derate – Immediate D2 – Unplanned (Forced) Derate – Delayed D3 – Unplanned (Forced) Derate – Postponed MO – Maintenance Outage

Figure 3-2

Processed NERC-GADS Data for Each Forced Outage Event on the Left, and the Consolidated Data for Each Plant/Unit/Cause Code by Year on the Right for the Small System

On the right side of Figure 3-2 is the consolidated forced outage event data by “Plant/Unit/Cause Code” by “Year.” Note that the number of forced outage occurrences is counted and the

equivalent hours are totaled.

Risk Rank and Risk Plot

The consolidated data are copied and pasted into the risk-rank workbook as shown on the left side in Figure 3-3. Note that the “Total Annual MWH Loss” for “Plant/Unit/Cause Code” for each year is calculated. Under the “Tools” menu, “Aggregate” is selected, and the aggregated data by “Plant/Unit/Cause Code” for all years appears on the right.

Figure 3-3

Small System Input Data in the “Raw Data” Tab in the Risk-Rank Workbook Is on the Left, and the Plant/Unit/Cause Code for All Years Is Shown on the Right

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System Examples

3-4

The aggregated data are then copied and pasted into the “Rank” tab, where risk is calculated and then all the data are sorted by risk in descending order, as shown in Figure 3-4. At this point, we have the “Plant/Unit/Cause Codes” sorted by risk for the system.

Figure 3-4

Risk-Ranked System Data for the Small System

All four columns of the risk-ranked data are now pasted into the risk-plot workbook, as shown in Figure 3-5. Note that the risk rank and cumulative risk of each “Plant/Unit/Cause Code” are determined and presented in the right-most columns.

Figure 3-5

Figure 3-6

Log-Log Risk Plot of Small System Forced Outage Data by Plant/Unit/Component Cause Code With the Line of Constant Risk Just to the Left of the 27 Risk-Critical Points

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System Examples

3-6

Select

Based on the rapid reduction of incremental cumulative risk as you increase in rank at the 27th ranked component, the top 27 risk components were chosen as risk-critical for this system at this time. To produce the final version of Figure 3-7, the component identifiers are changed to one  blank for all components, the plot points are relabeled, and then the identifiers for the top 27

components are replaced and the points relabeled again. These labeled points assist in the  placement of the line of constant risk.

Copying the four columns of the risk-critical components into another worksheet, sorting them  by component identifier, and summing the total risk from these components for each unit in

Figure 3-8 provide a method of selection of Unit D1 as the unit needing an outage business plan using the Boiler OIO. Note that Unit D1 is the unit with the highest risk.

Figure 3-8

The Small System Risk-Critical Components Sorted by Plant/Unit/Component Cause Code

Estimate

The probability-of-failure curves for the run case or “without overhaul” case were generated for the 10 components that were selected as critical in Figure 3-8 for Unit D1. Only the last six years of forced outage data were used to generate the probability curves, because these were all that were available. The cause codes in Table 3-1 were dropped because an overhaul plan was not appropriate for them or they had already been addressed.

4580 Generator end bells and bolting

9290 Other fuel-quality problems

1700 Feedwater controls

8600 Flue gas additive

Figure 3-9 shows the annual probability change calculated for Cause Code 9630, “Opacity,” in workbook ProbCalc. The data for the three columns on the left came from columns A, B, and C of the system risk-rank workbook (see Figure 3-3). The “Operation Year” and “Probability Change by Year” for this component were copied and pasted into the “Fit of History” tab of Baycom11 in Figure 3-10. After entering a base year, a Weibull curve fit is performed on these data by clicking “Fit of History” on the “Tools” menu, and the history and curve fit data are  produced as shown in Figure 3-11. If the fit is not satisfactory, then modify the base year in

Baycom11, cell G2, until a satisfactory fit is obtained. Once all component curves are fitted, then the Weibull alpha, beta, and base year values for this “without overhaul” probability curve can  be input for each component into the Boiler OIO.

Figure 3-9

ProbCalc Workbook Calculating the Change in Probability by Year for Cause Code 9630, Opacity

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Figure 3-10

Input Worksheet in Baycom11, “Fit of History” Tab, Where the Weibull Shape (Alpha) and Scale (Beta) Parameters Are Calculated for Input Into the Boiler OIO for Cause Code 9630, Opacity

Figure 3-11

The Cumulative Probability-of-Failure Plot in Baycom11 That Compares the Weibull Fitted Curve to a Curve Linking the Failure History Points for Cause Code 9630, Opacity

There were three tube failures for component 1000, “Furnace Wall,” which is insufficient to  perform a Weibull curve fit. For this component, a probabilistic opinion interview was conducted

using the same process as the software tool STACKER. The results are shown in Figure 3-12 for the run and overhaul case.

Figure 3-12

Results From the Probabilistic Opinion Interview for the Projected Probability of Failure for Unit D1 Furnace Wall

The “Operation Year” and “Probability Change by Year” for this component were copied and  pasted into the “Fit of Interview” tab of Baycom11, as shown in Figure 3-13. After entering a  base year, a Weibull curve fit is performed on these data by clicking “Fit of Interview” on the

“Tools” menu, and the interview and curve fit data are produced as shown in Figure 3-14. If the fit is not satisfactory in the judgment of the analyst, then modify the base year in Baycom11, cell G2, until a satisfactory fit is obtained.

Figure 3-13

Input Worksheet In Baycom11, “Fit of Interview” Tab, Where the Weibull Shape (Alpha) and Scale (Beta) Parameters Are Calculated for Input Into the Boiler OIO for Cause Code 1000, Furnace Wall

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Figure 3-14

The Cumulative Probability-of-Failure Plot “Without Overhaul” in Baycom11 That

Compares the Weibull Fitted Curve to a Curve Linking the Interview Points for Cause Code 1000, Furnace Wall

The fitted cumulative probability-of-failure “without overhaul” curves versus future year curves for the 10 component cause codes on Unit D1 are shown in Figure 3-15.

Figure 3-15

The Resulting Fitted Probability-of-Failure “Without Overhaul” Curves for the 10 Risk-Critical Components Included in the Boiler OIO Analysis

component between the outage and the retirement of the unit, except for component 1000, “Furnace Wall.” This can be a good first analysis approach, unless the value of the outage is considered marginal; then the “with overhaul” probability needs to be generated. This probability usually comes from an opinion interview. For each component with the no-forced outage

assumption in the “With Overhaul” tab, the Weibull shape parameter is input as 12 and the Weibull scale parameter is input as 1000 to make the resulting probability-of-failure curve zero. For the component 1000, “Furnace Wall,” the results from the opinion interview were used for the “with overhaul” case.

Figure 3-16

Boiler OIO Input “With Overhaul” for Unit D1

The inputs for the “Without Overhaul” tab are shown in Figure 3-17. These inputs were derived from the history data for these 10 components and processed with ProbCalc and Baycom11.

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Figure 3-17

Boiler OIO Input “Without Overhaul” for Unit D1

The operation parameters by year (unit replacement power cost, projected capacity factor, and service factor) were input in their respective tabs, as well as the financial assumptions for time value of money and taxes. The annual budget limits, forced outage rate limit, and probability of safety flag limit are input in their respective columns in the “Summary” tab, as shown in Figure 3-18. After loading all the data, the “Launch Optimization” button is clicked and the outage  business plan for Unit D1 is produced. Figure 3-18 shows the overhaul year that will produce the

highest net present value (NPV) within the constraints and when and if the safety limit is

exceeded. To the right top, the totals of the present-value cash flows for this analysis period (in this case, 20 years) for the outage performed in 2003 are shown. These after-tax, present-value totals come from columns D, F, and K, respectively. The current-value totals before taxes are shown to the left of each of these columns. To the far right is a series of total expected NPVs for the overhaul being conducted in each of the respective years of the analysis period.

Figure 3-18

Figure 3-19

Boiler OIO NPV Versus Overhaul Year Results for Unit D1

Examine

Upon examination of Boiler OIO inputs for the two components for Unit D1, it was determined that projected service factor is constant at around 75%, and the projected replacement energy value is rising linearly with time. Note that the probability-of-failure curve for the 3440 – High-Pressure Heater Tube Leaks, shown in Figure 3-15, is rising fairly linearly. Examination of the expected consequential cash flows, Figure 3-20, for this component, indicates that it is the highest consequential cost component. For this reason, the NPV curve shape is driven by the curve shape of the probability curve for component 3440, high-pressure heater tube leaks,  because the service factor and replacement values are linear. The large service factor, the large

number of tubes in the high-pressure heater, the large forced outage duration for these tube

failures, and a unit expected retirement date significantly beyond the end of the analysis window result in a large NPV for all years.

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Figure 3-20

Boiler OIO Cost to Overhaul Worksheet Showing the Annual Consequential Cost for the 10 Components Selected for Unit D1

Figure 3-15 shows the Weibull projected curves for the run case or “without overhaul” case for the Cause Code 9630, “Opacity.” Note that the projected probability curve reached a cumulative value of one in 2003 for the run case. It would be expected that the component that continues to  be run would have a continuing rising probability-of-failure curve resulting in a higher calculated

cost without overhaul. This is a current weakness in using this form of Weibull curve projection for large and rapidly rising probabilities for components that are developing significant

degradation, as shown in Figure 3-11. This situation with the probability curves for these components will be addressed in future developments of this process. For now, the

recommendation would be to project the future probability of failure from a probabilistic opinion interview using STACKER with plant personnel.

Conclusion

Unit D1 needs an overhaul as soon as possible to reap a $123,000,000 net present value savings. Some delay in the overhaul will not have a serious consequence because the NPV versus

overhaul year curve is of such low slope.

The high-pressure heater with a reasonably high increasing annual rate of probability of failure dominated the value for the timing of an overhaul with its large tube population and the duration of a forced outage from a tube leak large at 77 hours.

selected for overhaul timing optimization. Again, we will determine when the overhaul is needed selected for overhaul timing optimization. Again, we will determine when the overhaul is needed for these units and what components need to be involved in the overhauls.

for these units and what components need to be involved in the overhauls.

Analysis Process Analysis Process

Gather Gather

The last five years of NERC-GADS submissions were downloaded

The last five years of NERC-GADS submissions were downloaded as shown in Figure 3-21. Theas shown in Figure 3-21. The

last five years of forced outage data were selected by the power company to represent the recent last five years of forced outage data were selected by the power company to represent the recent  problems on this system.

 problems on this system.

Figure 3-21 Figure 3-21

A Portion of the System NERC-GADS Submissions Forced Outage Event Data for the A Portion of the System NERC-GADS Submissions Forced Outage Event Data for the Large System

Large System

Process Process

The raw data were processed by removing all but U1-U3, D1-D3, and MO type of outages

The raw data were processed by removing all but U1-U3, D1-D3, and MO type of outages22. The. The

zero equivalent hour entries were deleted. The columns headed “Unit/Cause Code,” “Year,” “Net zero equivalent hour entries were deleted. The columns headed “Unit/Cause Code,” “Year,” “Net 2

2

 U1 – Unplanned (Forced) Outage – Immediate  U1 – Unplanned (Forced) Outage – Immediate

U2 – Unplanned (Forced) Outage – D U2 – Unplanned (Forced) Outage – Delayedelayed U3 – Unplanned (Forced) Outage –

U3 – Unplanned (Forced) Outage – PostponedPostponed D1 – Unplanned (Forced) Derate – Immediate D1 – Unplanned (Forced) Derate – Immediate D2 – Unplanned (Forced) Derate – Delayed D2 – Unplanned (Forced) Derate – Delayed

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3-16 3-16

Maximum Capacity,” and “Equivalent Hours” were sorted by “Unit/Cause Code” and “Year” in Maximum Capacity,” and “Equivalent Hours” were sorted by “Unit/Cause Code” and “Year” in descending order. The data for these individual forced outage events are shown in the left side of descending order. The data for these individual forced outage events are shown in the left side of Figure 3-22. On the right of this figure is the consolidated forced outage event data by

Figure 3-22. On the right of this figure is the consolidated forced outage event data by “Unit/Cause Code” by “Year” showing the total annual number of occurrences and total “Unit/Cause Code” by “Year” showing the total annual number of occurrences and total equivalent hours.

equivalent hours.

The raw data were processed by removing all but U1-U3, D1-D3, and MO type of outages

The raw data were processed by removing all but U1-U3, D1-D3, and MO type of outages33. The. The

zero equivalent hour entries were deleted. The columns headed “Unit/Cause Code,” “Year,” “Net zero equivalent hour entries were deleted. The columns headed “Unit/Cause Code,” “Year,” “Net Maximum Capacity,” and “Equivalent Hours” were sorted by “Unit/Cause Code” and “Year” in Maximum Capacity,” and “Equivalent Hours” were sorted by “Unit/Cause Code” and “Year” in descending order. The data for these individual forced outage events are shown in the left side of descending order. The data for these individual forced outage events are shown in the left side of Figure 3-22. On the right of this figure is the consolidated forced outage event data by

Figure 3-22. On the right of this figure is the consolidated forced outage event data by “Unit/Cause Code” by “Year” showing the total annual number of occurrences and total “Unit/Cause Code” by “Year” showing the total annual number of occurrences and total equivalent hours.

equivalent hours.

Figure 3-22 Figure 3-22

Processed NERC-GADS Data for Each Forced Outage Event on the Left and the Processed NERC-GADS Data for Each Forced Outage Event on the Left and the Consolidated Data for Each Plant/Unit/Cause Code by Year on the Right for the Large Consolidated Data for Each Plant/Unit/Cause Code by Year on the Right for the Large System

System

3 3

 U1 – Unplanned (Forced) Outage – Immediate  U1 – Unplanned (Forced) Outage – Immediate

U2 – Unplanned (Forced) Outage – D U2 – Unplanned (Forced) Outage – Delayedelayed U3 – Unplanned (Forced) Outage –

U3 – Unplanned (Forced) Outage – PostponedPostponed D1 – Unplanned (Forced) Derate – Immediate D1 – Unplanned (Forced) Derate – Immediate D2 – Unplanned (Forced) Derate – Delayed D2 – Unplanned (Forced) Derate – Delayed D3 – Unplanned (Forced) Derate – Postponed D3 – Unplanned (Forced) Derate – Postponed MO – Maintenance Outage

MO – Maintenance Outage SF – Startup Failure

all years is shown on the

all years is shown on the right after running the aggregation macro.right after running the aggregation macro.

Figure 3-23 Figure 3-23

Large System Input Data in the “Raw Data” Tab in the Risk-Rank Workbook Is on the Left, Large System Input Data in the “Raw Data” Tab in the Risk-Rank Workbook Is on the Left, and the Plant/Unit/Cause Code for All Years Is Shown on the Right

and the Plant/Unit/Cause Code for All Years Is Shown on the Right

The aggregated data are copied and “paste special/value” pasted into the “Rank” Tab, where they The aggregated data are copied and “paste special/value” pasted into the “Rank” Tab, where they are then sorted by risk in descending order as shown in Figure 3-24.

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All four columns of data are now copied and “paste special/value” pasted into the risk-plot workbook as shown in Figure 3-25. Note that the risk rank and cumulative risk of each “Plant/Unit/Cause Code” is determined on the right-most columns.

Figure 3-25

Risk-Ranked Data Inserted Into Risk-Plot Workbook

From this risk-ranked data, a log-log risk plot was produced as shown in Figure 3-26, and the diminishing-risk plot is shown in Figure 3-27 by clicking the “Label Plot Points” button.

Figure 3-26

Log-Log Risk Plot of Large System Forced Outage Data by Plant/Unit/Component Cause Code

Figure 3-27

Diminishing-Risk Plot for Large System Showing up to the 25th Ranked Component as the Highest Contributors to Incremental Cumulative Risk

Select

The rapid reduction of incremental cumulative risk occurred with the 25th ranked component as indicated in Figure 3-27. Based on the rapid reduction of incremental cumulative risk as you increase in rank at the 25th ranked component, the top 25 risk components were chosen as risk-critical for this system at this time. To produce the final version of Figure 3-26, the component identifiers are changed to one blank for all components, the plot points are relabeled, and then the identifiers for the top 25 components are replaced and the points are relabeled again. These labeled points assist in the placement of the line of constant risk.

The four columns of the risk-critical components, sorted by component identifier, are shown in Figure 3-28. The total risks for components associated with each unit are also shown. Units AL2, R1, and AJ6 have the highest risk. In this case, the power company chose to perform a Boiler OIO analysis on Units R1 and W1 at this time. The second reheater (component 1070) on Unit R1 has been replaced and was removed from consideration. Also, the second superheater

(component 1050) on Unit W1 has been replaced and was removed from consideration. Note that in Figure 3-28, Units R1 and W1 were selected for the boiler OIO analysis, even though they are not the highest risk.

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Figure 3-28

The Large System Risk-Critical Components Sorted by Plant/Unit/Component Cause Code

Estimate

For Unit R1

The probability-of-failure curves for the run or “without overhaul” case were generated for the remaining two risk-critical components to be considered on Unit R1 using 14 years of failure-history data because these data were all that were available. The annual change in probability is calculated for component cause code 1000, “Furnace Wall,” in Figure 3-29. In Figure 3-30, the operation year and probability change by year has been entered along with the base year. Then a Weibull curve can be fitted by selecting “Tools, Fit of History.” That will produce the

comparison plot in Figure 3-31. Again, if the fit is not satisfactory, then change the base year and try a refit. When all component curves are fitted, then the Weibull alpha, beta, and base year values for this “without overhaul” probability curve can be input for each component into the Boiler OIO. The resulting probability-of-failure versus time curve used in the Boiler OIO analysis of Unit R1 for the “without overhaul” case in shown in Figure 3-32.

Figure 3-29

ProbCalc Workbook Calculating the Change in Probability by Year for Cause Code 1000, Furnace Wall

Figure 3-30

Input Worksheet in Baycom11, “Fit of History” Tab, Where the Weibull Shape and Scale Parameters Are Calculated for Input Into the Boiler OIO for Cause Code 1000, Furnace Wall

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Figure 3-31

The Cumulative Probability-of-Failure Plot in Baycom11 That Compares the Weibull Fitted Curve to a Curve Linking the Failure History Points for Cause Code 1000, Furnace Wall

Figure 3-32

The Resulting Fitted Probability-of-Failure Curves for the Two Risk-Critical Components

For Unit W1

As for Unit R1, Figure 3-33 shows the annual change in probability for the component cause code 1040, “First Superheater” for Unit W1. Only 14 years of forced outage data were available for this unit also.

Figure 3-33

ProbCalc Workbook Calculating the Change in Probability by Year for Cause Code 1040, First Superheater

Figure 3-34 shows the resulting annual changes in probability in Baycom11 for this component for the run or “without overhaul.”

Figure 3-34

Input Worksheet in Baycom11, “Fit of History” Tab, Where the Weibull Shape and Scale Parameters Are Calculated for Input Into the Boiler OIO for Cause Code 1040, First Superheater

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Figure 3-35

The Cumulative Probability-of-Failure Plot in Baycom11 That Compares the Weibull Fitted Curve to a Curve Linking the Failure History Points for Cause Code 1040, First

Superheater

The resulting probability-of-failure versus time curves to be used in the Boiler OIO analysis of Unit W1 for the “without overhaul” case in shown in Figure 3-36.

Figure 3-36

Figure 3-37 with the other necessary unit input information. The option used for the “with overhaul” case in this analysis is to assume that there will be no forced outages or derates due to the component between the outage and the retirement of the unit. For each component in the “With Overhaul” tab, the Weibull shape parameter is input as 12, and the Weibull scale  parameter is input as 1000 to make the resulting probability-of-failure curve zero.

Figure 3-37

Boiler OIO Input “With Overhaul” for Unit R1

The inputs for the “Without Overhaul” tab are shown in Figure 3-38. These were the inputs that were derived from the history data for these two components and processed with ProbCalc and Baycom11.

Figure 3-38

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The operation parameters by year (unit replacement power cost), projected capacity factor, and service factor were input into their respective tabs as well as the financial assumptions for time value of money and taxes. The annual budget limits, forced outage rate limit, and probability of safety flag limit are input in their respective columns in the “Summary” tab, as shown in

Figure 3-39. After loading all the data, the “Launch Optimization” button is clicked and the outage business plan for Unit R1 is produced. This figure shows the overhaul year that will  produce the highest NPV within the constraints and when and if the safety limit is exceeded. To

the right top, the totals of the present value cash flows for this analysis period, in this case 20 years, for the outage performed in 2003 are shown. These present value totals come from

columns D, F, and K, respectively. The current value totals before taxes are shown to the left of each of these columns. To the far right is the total expected NPV for planning the overhaul in each year of the analysis period.

Figure 3-39

Boiler OIO Summary Worksheet for Unit R1

These overhaul year expected NPVs are plotted in Figure 3-40. Note the decrease in NPV if the outage was delayed.

Figure 3-40

Boiler OIO NPV Versus Overhaul Year Results for Unit R1

For Unit W1

The inputs for the two components for Unit W1 for the “with overhaul” case are shown in Figure 3-41 with the other necessary unit input information. The option used for the “with

overhaul” case (component replacement) in this analysis is to assume that there will be no forced outages or derates due to the component between the outage and the retirement of the unit. For each component in the “With Overhaul” tab, the Weibull shape parameter is input as 12, and the Weibull scale parameter is input as 1000 to make the resulting probability-of-failure curve zero.

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The inputs for the “Without Overhaul” tab are shown in Figure 3-42. These were the inputs that were derived from the history data for these two components and processed with ProbCalc and Baycom11.

Figure 3-42

Boiler OIO Input “Without Overhaul” for Unit W1

The operation parameters by year—that is, unit replacement power cost, projected capacity factor, and service factor—were input in their respective tabs as well as the financial

assumptions for time value of money and taxes. The annual budget limits, forced outage rate limit, and probability of safety flag limit are input in their respective columns in the “Summary” tab as shown in Figure 3-42. After loading all the data, the “Launch Optimization” button is clicked, and the outage business plan for Unit W1 is produced. This figure shows the overhaul year that will produce the highest NPV within the constraints and when and if the safety limit is exceeded. To the right top, the totals of the present value cash flows for this analysis period, in this case 20 years, for the outage performed in 2003 are shown. These present value totals come from columns D, F, and K, respectively. The current value totals before taxes are shown to the left of each of these columns. To the far right is the total expected NPV for planning the overhaul in each year of the analysis period.

Figure 3-43

Figure 3-44

Boiler OIO NPV Versus Overhaul Year Results for Unit W1

Examine

For Unit R1

Upon examination of Boiler OIO inputs for the two components for Unit R1, it was determined that projected service factor is constant at around 79%, and projected replacement energy value is rising linearly with time. Note that the two probability-of-failure curves in Figure 3-32 are fairly linear as well. Examining the expected consequential cash flows for the two components, Figure 3-45, indicates the platen superheater begins lower and then is higher than the furnace wall, which is a reflection of the probability curves in Figure 3-32 and their domination of the annual cash flows. The large service factor plus the rapid near linear rise in the probability-of-failure curve and the linearly increasing replacement energy values create a linearly decreasing  NPV if the overhaul is delayed on Unit R1. However, note that a unit expected retirement date

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Figure 3-45

Boiler OIO Cost “Without Overhaul” Worksheet Showing the Annual Consequential Cost for the Two Components Selected for Unit R1

For Unit W1

A similar examination of the Boiler OIO inputs for the two components for Unit W1 revels that  projected service factor is constant at around 74%, and projected replacement energy value is  just as with Unit R1. The two probability-of-failure curves in Figure 3-36 are again fairly linear

as with Unit R1. In the case of Unit W1, the curve for the first superheater, 1040, is an order of magnitude lower. Examining the expected consequential cash flows for the two components, Figure 3-46, indicates that they are quite similar. Even though the probability curves are quite different, the populations of tubes are quite different. The number of tube elements is much greater, 7904 for the first superheater as opposed to 2528 for the economizer. This raises the contribution of the low change in probability per year by a factor of three, resulting in there not  being a great difference in the expected consequential cost per year for the two components.

The large service factor plus the rapid near linear rise in the probability-of-failure curve and the linearly increasing replacement energy values create a linearly decreasing NPV if the overhaul is delayed on Unit W1. In this case, the unit expected retirement date is near the end of the analysis window, so there is only a minor contribution to the NPV.

Figure 3-46

Boiler OIO Cost “Without Overhaul” Worksheet Showing the Annual Consequential Cost for the Two Components Selected for Unit W1

For Unit R1, the fairly rapid rise of the probability-of-failure curve for the platen superheater together with a rise in the projected replacement energy values on a 325-MW unit with 72 hours down per forced outage results in continued run or “without overhaul” consequences that

overcomes the time value of money advantage of delaying the overhaul.

Unit W1 needs an overhaul as soon as possible to reap a $27,000,000 net present value savings. Some delay in the overhaul will have some consequence because the NPV versus overhaul year curve is of a moderate slope.

With Unit W1, the probability-of-failure curve rise with the same replacement energy values, 184-MW capacity and 72 hours down per forced outage, results in a continued run or overhaul consequences that overcome the time value of money advantage of delaying the overhaul. The larger NPV for Unit R1 than Unit W1 in Figures 3-40 and 3-44, respectively, is caused by there being almost a two-to-one capacity difference between Unit R1 and Unit W1. This will result in about a two-to-one lower NPV for Unit W1 than R1 for the optimal overhaul year of 2003 for both units.

Large Plant Example

You may be concerned about planning a boiler overhaul at the plant level instead of at the

system level. This shows that even on a plant level, with an example of four units, the process is the same as that for a system—there are just fewer history data to process. The risk analysis steps will still go from the determination of risk-critical components and which units have the highest risk components. A unit will be selected for overhaul optimization. We will determine when the overhaul is needed for this unit and what components need to be involved in the overhaul.

Analysis Process

Gather

The last five years of NERC-GADS submissions were downloaded as indicated in Figure 4-1. These forced outage data were selected to represent the recent problems on this plant.

Figure 4-1

A Portion of the System NERC-GADS Submissions Forced Outage Event Data for the Large Plant

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4-2

Process

The raw data were processed by removing all but U1-U3 and D1-D3 outage types4. The zero

equivalent hour entries were deleted. The columns headed “Unit/Cause Code,” “Year,” “Net Maximum Capacity,” and “Equivalent Hours” were sorted by “Unit/Cause Code” and “Year” in descending order. The data for these individual forced outage events are shown in the left side of Figure 4-2. On the right of this figure is the consolidated forced outage event data by

“Unit/Cause Code” by “Year” showing the total annual number of occurrences and total equivalent hours.

Figure 4-2

Processed NERC-GADS Data for Each Forced Outage Event on the Left and the Consolidated Data for Each Unit/Cause Code by Year on the Right for the Large Plant

Risk Rank and Risk Plot

All columns and rows of data from the right side of Figure 4-2 were copied and “paste special/ value” pasted into the yellow background area of the risk-rank workbook as shown in Figure 4-3 on the left side. The aggregation of last five years of data by “Unit/Cause Code” for all years is shown on the right after running the aggregation macro.

4

 U1 – Unplanned (Forced) Outage – Immediate U2 – Unplanned (Forced) Outage – Delayed U3 – Unplanned (Forced) Outage – Postponed D1 – Unplanned (Forced) Derate – Immediate D2 – Unplanned (Forced) Derate – Delayed D3 – Unplanned (Forced) Derate – Postponed MO – Maintenance Outage

Figure 4-3

Large Plant Input Data in the “Raw Data” Tab in the Risk-Rank Workbook Is on the Left and the Unit/Cause Code for All Years Is Shown on the Right

The aggregated data are copied and “paste special/value” pasted into the Rank tab, where it is then sorted by “Risk” in descending order as shown in Figure 4-4.

Figure 4-4

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4-4

All four columns of data are now copied and “paste special/value” pasted into the risk-plot workbook as shown in Figure 4-5. Note that the risk rank and cumulative risk of each unit/cause code are determined on the right-most columns.

Figure 4-5

Risk-Ranked Data Inserted Into Risk-Plot Workbook

From these risk-ranked data, a log-log risk plot was produced, as shown in Figure 4-6, and the diminishing-risk plot in Figure 4-7 by clicking the “Label Plot Points” button.

Figure 4-6

Figure 4-7

Diminishing-Risk Plot for Large Plant Showing up to the 21 Ranked Components as the Highest Contributors to Incremental Cumulative Risk

Select

The rapid reduction of incremental cumulative risk occurred with the 21st ranked component as indicated in Figure 4-7. Based on the rapid reduction of incremental cumulative risk as you increase in rank at the 21st ranked component, the top 21 risk components were chosen as risk-critical for this system at this time. To produce the final version of Figure 4-7, the component identifiers are changed to one blank for all components, the plot points are relabeled, and then the identifiers for the top 21 components are replaced and the points relabeled again. These labeled points assist in the placement of the line of constant risk.

The four columns of the risk-critical components, sorted by component identifier, are shown in Figure 4-8. The total risk for components associated with each unit is also shown. Unit R3 stands out as having the highest risk. We will pursue the planning of this units boiler overhaul in this example.

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Figure 4-8

The Large Plant Risk-Critical Components Sorted by Unit/Component Cause Code

Estimate

The probability-of-failure curves for the run or “without overhaul” case were generated for the six critical components to be considered on Unit R3 from the last 22 years of probability-of-failure data. The 22 years goes back to the beginning of NERC-GADS company submissions, as well as it represents the entire life of these components. The annual change in probability is calculated for component cause code 1000, Furnace Wall, in Figure 4-9. In Figure 4-10, the operation year and probability change by year has been entered along with the base year. Then a Weibull curve can be fitted by selecting “Tools, Fit of History.” That will produce the

comparison plot in Figure 4-11. Again, if the fit is not satisfactory, then change the base year and try a refit. When all component curves are fitted, then the Weibull alpha, beta, and base year values for this “without overhaul” probability curve can be input for each component into the Boiler OIO.

Figure 4-9

ProbCalc Workbook Calculating the Change in Probability by Year for Cause Code 1000, Furnace Wall

Figure 4-10

Input Worksheet in Baycom11, “Fit of History” Tab, Where the Weibull Shape and Scale Parameters Are Calculated for Input Into the Boiler OIO for Cause Code 1000, Furnace Wall

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Figure 4-11

The Cumulative Probability-of-Failure Plot in Baycom11 That Compares the Weibull Fitted Curve to a Curve Linking the Failure History Points for Cause Code 1000, Furnace Wall The resulting probability-of-failure versus time curve to be used in the Boiler OIO analysis of Unit R3 for the “without overhaul” case is shown in Figure 4-12.

Figure 4-12

The Resulting Fitted Probability-of-Failure “Without Overhaul” Curves for the Six Risk-Critical Components That Remained After Examination of the Applicability of Including Cause Codes in the Boiler OIO Analysis

the component between the outage and the retirement of the unit. For each component in the “With Overhaul” tab, the Weibull shape parameter is input as 12, and the Weibull scale  parameter is input as 1000 to make the resulting probability-of-failure curve zero.

Figure 4-13

Boiler OIO Input “With Overhaul” for Unit R3

The inputs for the “Without Overhaul” tab are shown in Figure 4-14. These inputs were derived from the history data for these six components and processed with ProbCalc and Baycom11.

Figure 4-14

 EPRI Licensed Material

 Plant Example

4-10

The operation parameters by year (unit replacement power cost, projected capacity factor, and service factor) were input in the respective tabs as well as the financial assumptions for time value of money and taxes. The annual budget limits, forced outage rate limit, and probability of safety flag limit are input in the respective columns in the “Summary” tab as shown in

Figure 4-15. After loading all the data, the “Launch Optimization” button is clicked and the outage business plan for Unit R3 is produced. This figure shows the overhaul year that will  produce the highest NPV within the constraints and when and if the safety limit is exceeded. To

the right top, the totals of the present value cash flows for this analysis period, in this case 20 years, for the outage performed in 2003 is shown. These present value totals come from columns D, F, and K, respectively. The current value totals before taxes are shown to the left of each of these columns. To the far right is the total expected NPV for planning the overhaul in each year of the analysis period.

Figure 4-15

Boiler OIO Summary Worksheet for Unit R3

These expected NPVs by overhaul year are plotted in Figure 4-16. Note the decrease in NPV if the outage was delayed.

Figure 4-16

Boiler OIO NPV Versus Overhaul Year Results for Unit R3

Examine

Figure 4-17

Boiler OIO Cost “With Overhaul” Worksheet Showing the Annual Consequential Cost for the Six Components Selected for Unit R3