Asset Management and System Reliability Group …quanta-technology.com/.../files/doc-files/InfraSourceFinalReport.pdf · Final Report: Asset Management and System Reliability Group

Final Report:

Asset Management and System Reliability Group Review

Prepared for: Southern California Edison Prepared by: InfraSource Technology Contact: Richard Brown, PhD, PE [email protected] 4020 Westchase Blvd., Suite 375 Raleigh, NC 27607 919-334-3021 (V) 919-961-1019 (M) May 17th 2007

SCE AMSR Group Review FINAL REPORT Page 2 of 28

Executive Summary

The Asset Management and System Reliability Group (AMSR) of Southern California Edison (SCE) has requested an independ-ent review of its objectives, methodologies, and resources. InfraSource has accomplished this through a review of data provided by SCE and a series of on-site interviews. The InfraSource assessment focuses on reliability assessment, aging infrastructure assessment, infrastructure replacement planning, and credibility of results with respect to internal budgeting and general rate case funding requests. AMSR has oversight responsibilities in three categories of spending: the replacement of subsurface switches, worst circuit reha-bilitation, and proactive cable replacement. In each of these areas, AMSR is making appropriate spending recommendations as benchmarked against the requirements of asset management, the overall objectives of Southern California Edison, and the ap-proaches of other large investor-owned utilities in the United States. This finding is based on an assessment of organization, data, reliability management, and aging cable models. Organization. The AMSR group is situation organizationally within SCE in a manner suitable for its stated mission and goals. AMSR is slightly understaffed, and will increasingly require higher levels of staffing as the amount of proactive equipment re-placement activity increases. Data. The data available to AMSR for making proactive equipment replacement decisions is better than at most large utilities. This is especially true for equipment population age data. The present outage management system has only been active since the beginning of 2006, and data from this system must currently be supplemented from an old system (the data is not as complete in the old system). This limitation is being well-managed, and data from the old system will eventually not be needed by AMSR. Reliability Management. AMSR is appropriately considering all capital aspects of potential reliability improvement projects for both overhead and underground. In addition, AMSR is properly coordinating with other groups within SCE to ensure that the most appropriate projects are identified. AMSR does not presently recommend inspection or maintenance projects. However, it is the plan of AMSR to include inspection and maintenance work in its work scope for 2009 and beyond, which will increase its ability to address the reliability in a cost effective manner. Aging Cable Models. AMSR has advanced cable failure rate models and aging system reliability models when compared to the industry as a whole. The resulting predictions have the opportunity to become more specific over time with better data and better analytical models, but the current predictions for escalating cable failures and the corresponding impact to system reliability are able to identify appropriate levels of cable replacement and appropriate replacement projects. SCE will have to significantly in-crease the amount of proactive cable replacement in the near future to avoid significant worsening of system reliability. For higher levels of cable replacement that will be seen in five years and beyond, it will become increasingly desirable to use more detail data and more detailed models. The data and methodologies used by AMSR are appropriate and have resulted in credible reliability predictions with regards to increasing cable failures, the impact of increasing cable failures on system reliability, and the impact of proactive cable replace-ment on future reliability. The approach of AMSR for proactive equipment replacement is more sophisticated and produces re-sults with higher confidence when compared to most other large utilities. Spending requests by AMSR are reasonable, higher spending request levels could be justified at this time, and higher levels of spending on proactive equipment replacement will be required in the future.


Table of Contents

EXECUTIVE SUMMARY .........................................................................................................................................2

1 INTRODUCTION ..............................................................................................................................................4

2 ORGANIZATION ..............................................................................................................................................5

3 RELIABILITY DATA AND REPORTING.....................................................................................................7

4 RELIABILITY MANAGEMENT...................................................................................................................12

5 PROACTIVE CABLE REPLACEMENT......................................................................................................15

6 CONCLUSIONS AND RECOMMENDATIONS ..........................................................................................20

APPENDIX A: QUALIFICATIONS OF RICHARD E. BROWN........................................................................21


1 Introduction

The Asset Management and System Reliability Group (AMSR) of Southern California Edison (SCE) has requested an independent review of its objectives, resources, methodologies, and resources. InfraSource has accomplished this through a review of data provided by SCE and a series of on-site interviews con-ducted by Richard Brown at the Santa Ana SCE site. This report is presents the results of Dr. Brown’s findings. The AMSR Group of Southern California Edison (SCE) has the following mission: AMSR Mission

Asset Management & System Reliability will enhance distribution reliability by ensuring timely, suf-ficient, and cost-effective replacements of aging infrastructure, by impelling cost-effective improve-ments in system design, maintenance, and operation, by providing data and analysis to organizations whose activities impact system reliability.

Specific objectives of the Asset Management and System Reliability Group are the following: AMSR Objectives

• With a quantitative measure of uncertainty and employing auditable analyses, predict reliability over the next 25 years at the system and circuit level, with and without various levels of preemp-tive infrastructure replacement and/or automation.

• Utilize the above analyses to (1) enable informed decision-making at the senior and executive management levels regarding resource expenditures, and (2) support, with empirical evidence, SCE’s request for capital funding of infrastructure replacement in the 2009 General Rate Case.

Considering the above mission and objectives, the InfraSource assessment focuses on reliability assess-ment, aging infrastructure assessment, aging infrastructure planning, and credibility of results with respect to internal budgeting and general rate case funding requests. As such, this report first describes the AMSR group in terms of the greater SCE organization. It then discusses data, modeling, and reliability manage-ment. Last, it discusses the AMSR approach to proactive reliability management in general and proactive cable replacement in detail.


2 Organization

Southern California Edison (SCE) is one of the largest investor-owned electric utilities in the United States. It serves more than 13 million people in a 50,000 square-mile area of central, coastal and Southern California, excluding the City of Los Angeles and certain other cities. SCE has consolidated assets of ap-proximately $25.3 billion, and employs about 15,000 people. When examining a relatively small group like AMSR, it is critical to examine its place within the greater corporate organization. In this case, AMSR falls within the SCE Transmission and Distribution business unit within the Engineering and Technical Services division. The SCE organizational chart showing AMSR is shown in Figure 2-1.

Figure 2-1. SCE Organizational Chart


As shown in Figure 2-1, AMSR is located under both planning and engineering, but excludes both field engineering and transmission planning. Therefore, the AMSR group is organizationally suitable for ad-dressing issues related to distribution planning, reliability, and proactive infrastructure replacement. This organizational structure supports the AMSR mission and goals as stated in Section 1. Not visible on the organizational chart is the fact that AMSR is only responsible for examining issues im-pacting capital budgets (as opposed to inspection, maintenance, and/or operational budgets). This is typi-cal industry practice. There are potential benefits for an asset management group being responsible for both capital and expense budgets, but this is not common industry practice at the present time. AMSR is presently in the process of proposing the addition of some expense dollars to the worst circuit rehabilita-tion program. Because AMSR is only responsible for examining issues impacting capital budgets, the remainder of this report will focus primarily on projects and issues that impact capital budgets. Within AMSR, there is a manager and eight additional people. Of these eight people, two are focused on asset model development, three are focused on reliability data collection and reporting, and three are fo-cused on project identification and scoping. Together, this group is responsible for identifying and justify-ing about $30 million in project work for 2006, ramping up to roughly $90 million in project work for 2009. AMSR is slightly understaffed for the present activity level of $90M in infrastructure replacement. As the activity level increases, the need for a higher level of staffing will increase. There is no obvious way to automate present job tasks. In fact, the current AMSR plan to utilize predictive reliability model-ing in its processes will further increase staffing needs (see Section 4).


3 Reliability Data and Reporting

System Reliability Data Prior to 2006, SCE collected reliability data through a system called DTOM, (Distribution Transmission Outage Management). This system was robust in data collection, but collected data primarily associated with total distribution circuit interruptions (referred to at “circuit outages”). This means that most inter-ruptions occurring downstream of fused lateral taps were not recorded in DTOM. At SCE, interruptions occurring downstream of fused lateral taps are called “area outages.” At the beginning of 2006, SCE began using its new outage management system called ODRM, (Outage Database Reliability Metrics) to collect interruption data. This system is able to collect information on all distribution interruptions, including area outages. Based on data collected from ODRM, SCE regularly computes the following reliability indices: SAIFI, SAIDI, and MAIFI. These indices are appropriate for tracking high-level reliability trends for the SCE system. SCE is also able to (1) compute the percentage of indices due to circuit outages versus area outages, and (2) compute the percentage of indices due to weather, overhead equipment, underground equipment, vegetation, and so forth. This type of data report-ing capability is typical for the industry. At the time of this report, a little over 1 year of data from ODRM is available. ODRM is exclusively used for (1) computing the contributions of failures to SAIDI and SAIFI, and (2) computing mainline versus area outage ratios. A combination of DTOM and ODRM is used for cable outage data. It is appropriate at the present time for SCE to supplement ODRM data with DTOM data for asset man-agement decisions in the absence of sufficient ODRM data. However, AMSR should gradually move to-wards the exclusive use of ODRM data, which is its plan. Reliability data for 2006 by month is shown in Figure 3-1. This figure shows that consistently high con-tributions to overall SAIDI come primarily from overhead equipment, underground equipment, and third party issues. It also shows that weather contribution is highly variable from month to month. SCE is fur-ther able to break down these high-level causes into sub-categories. For example, a failure attributed to underground equipment can be further classified as to whether it is cable, and whether this cable failure was due to bad cable or to some other cause.


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Figure 3-1. 2006 SCE Reliability Data

In addition to tracking overall performance, SCE also tracks its “worst performing” circuits and identifies work to improve their reliability as part of its “worst circuit rehabilitation” (WCR) program. Unlike many utilities that use a single measure to track worst performing circuits, SCE creates several lists based on separate measures. For example, there is one list for each of the following metrics:

- Feeders contributing most to system SAIFI - Feeders contributing most to system SAIDI - Feeders with the most circuit outages - Feeders contributing most to outage frequency x peak feeder load / customers on feeder

Creating multiple lists identifies a group of feeders that (1) have a high impact to overall reliability, and/or (2) are likely to result in customer complaints. AMSR coordinates with field engineers (who have local knowledge of concerning circuits) to select the specific circuits to target for reliability improvement. This is a robust process and helps to ensure that WCR work is done on circuits that are truly problematic from a system, customer, and/or field operations perspectives. Equipment Reliability Data SCE has two computerized equipment management systems which work somewhat in parallel. The pri-mary function of the “DPI” system (Distribution Project Information) is to track physical assets. The pri-mary function of the “Passport” system is to schedule and document maintenance and inspections of those physical assets. When equipment is installed, removed, or replaced, DPI is updated via Work Orders. DPI contains information such as installation and/or removal dates, removal reasons, voltage, load rating, manufacturer, type, location, etc. This information is copied over to Passport on a daily basis. In addi-tional to the data from DPI, Passport also includes inspection requirements, schedules, and results, as well as schedules for replacement.


Figure 3-2. Sample DPI Work Order

Figure 3-3. Sample DPI Account Transaction Summary


A screen shot from a DPI work order is shown in Figure 3-2. This work order contains a material code, a location, an associated ledger account, and other useful information. Presently, there is not an easy way to access summary information from the DPI work orders, but AMSR should consider investigating this possibility. DPI work orders are reconciled to property record ledger accounts in the “CARS” system (Corporate Ac-counting Records System) via CPR (Continuing Property Records). There are underground cable ac-counts for each year, and each cable removal project results in the amount of removal being subtracted from the associated account. Neither cable locations nor removal reasons are recorded in CARS. CARS is also not able to segregate cable by voltage class (e.g., 4 kV, 12 kV, 16 kV) nor is it able to distinguish primary from secondary cable. However, this information is potentially accessible from DPI work order records. CARS contains data sufficient to calculate how much cable was installed in any given year, and how much of the cable installed in that year was removed in every subsequent year. Based on samples per-formed, SCE’s primary cable system is estimated to comprise 50% of the total distribution cable inven-tory (except PILC) with the remainder being the secondary cable system. Therefore, present calculations assume that half of each CARS account reflects primary cable and the other half reflects secondary cable. Because SCE (for the most part) has only purchased a single type of cable insulation in any given year, it can infer cable insulation type for each year. These cable types are PILC (paper insulated lead covered), HMW (high molecular weight), XLPE (cross-linked polyethylene), and TR-XLPE (tree retardant cross-linked polyethylene). From the data in CARS, SCE is able to produce a histogram of its current inventory of installed cable by year of installation (and therefore type). It is also able to estimate average cable fail-ure rates as a function of age. A chart showing cable age distribution based on accounting records is shown in Figure 3-4. The level of accuracy achieved by AMSR for population data is better than that achieved by many utili-ties. Most utilities are able to identify the amount of cable purchased each year from accounting records, but most do not reduce these accounts based on specific cable removal work orders. Instead, removal quantities are typically allocated across accounts using techniques such as Iowa curves. The cable population data presently available to AMSR is sufficient for most asset management func-tions. In addition, there is the potential to extract additional useful data from DPI.


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Figure 3-4. Primary Cable Age Histogram Based on Accounting Records

SCE has a set of Field Inventory Maps (FIMs) which are essentially scanned images showing a geo-graphic representation of distribution infrastructure. These maps contain useful information for certain types of equipment that are not available elsewhere. For example, the age, insulation type, conductor size, and voltage rating for each cable section is shown. FIMs are used when dates of failed equipment are not available, and to identify cables to proactively replace. This is done by displaying the appropriate map on a computer screen and visually examining the image, which can be time consuming. SCE has attempted to automatically capture information from these images with limited success thus far. In summary, the data sources used by AMSR are more complete than those used by many other large in-vestor-owned utilities in the United States. This data is appropriate and useful for both reliability report-ing and for making asset management decisions.


4 Reliability Management

Although electric utilities presently address asset management in many different ways, the best ap-proaches to asset management all contain several elements. These include the following: (1) decisions are data-driven; (2) decisions consider cost, performance, and risk; and (3) processes are in place to ensure that decisions are efficient. The present mandate of AMSR is to recommend capital projects that efficiently address reliability and risk concerns on the distribution system. Therefore, an appropriate working definition of asset manage-ment for AMSR is the following: Working Definition of Asset Management for the SCE AMSR Group

Asset management is a data-driven business approach that strives to identify appropriate levels of

infrastructure replacement that maximize reliability and risk benefits subject to financial and human

resource constraints.

AMSR makes certain spending recommendation based on risk. For example, AMSR recommends sub-surface switch replacements due to operational safety concerns. A specific category of oil-filled switches have been identified as vulnerable to catastrophic failure when operated. AMSR has prioritized these switches for replacement. AMSR is concerned with two aspects of reliability. First, it is interested in system reliability as measured by the reliability indices SAIFI, SAIDI, and MAIFI. Second, it is interested in worst circuit reliability. AMSR expects reliability indices to become worse over time due to aging infrastructure, and is develop-ing scenarios to address this problem (see Section 5). Worst circuits are a potential source of customer dissatisfaction, and are addressed through the previously-mentioned WCR program. Although AMSR is only concerned with capital budgets, it is useful from a benchmarking perspective to look at reliability holistically. The activities of AMSR can then be examined from this broader perspec-tive. This approach is now taken by (1) considering the reliability of overhead distribution, and (2) sepa-rately considering the reliability of underground distribution. The reliability of overhead distribution for SCE is driven by weather, overhead equipment failures, third-party-related failures, and trees. The major maintenance activities performed by SCE that can affect these areas are vegetation management and pole inspections (these help to reduce failures associated with weather, overhead equipment, and trees, but not third-party issues such as vehicular accidents). SCE is required by the California Public Utilities Commission (CPUC) to perform vegetation management in a specific manner. In addition, SCE is required by the CPUC to perform wood pole inspections in a specific manner. Given these factors, SCE has a limited influence on the reliability overhead circuits through changes in maintenance practices. As the overhead distribution equipment at SCE ages, reliability will generally not go down. Certain types of overhead equipment wear out over time (e.g., steel-core wire), but these types of failures generally only contribute a small amount to reliability indices. Similarly, aging pole-mounted transformers may begin to fail more often, but these failures only impact a small number of customers. Also, SCE has a transformer load management program that identifies heavily loaded distribution transformers that are likely to fail due to overloading. Aging wood poles in principal could begin to fail more often, but SCE is required (as previously mentioned) by the CPUC to periodically test and treat its wood poles. Aging infrastructure is


simply not a major issue for overhead SCE distribution circuits with regards to its impact on SAIFI, SAIDI, and MAIFI. Overhead reliability can be improved through capital projects such as lateral fusing, the addition of new manual switches, the addition of new automated switches, and the addition of new circuits. Typically the most cost effective capital approaches are lateral fusing, the addition of new manual switches, and auto-mation. With regards to these options, the following observations are made: Potential Capital Project to Improve Overhead Reliability

- More fuses. It is estimated that about half of all radial taps are fused. Fusing radial taps is pres-ently being done through the WCR program. Fusing radial taps on feeders not addressed by the WCR program is an opportunity for SCE.

- More switches. An interview with a system operator with 6 years of experience indicates that the number of manual switches on overhead circuits is sufficient for fault isolation and system resto-ration.

- More automation. SCE has a separate group in charge of the deployment of automated distribu-tion switches (for both overhead and underground). Presently SCE has about 2500 automated dis-tribution switches and an additional 1000 automated reclosers. Of the 4300 SCE distribution cir-cuits, this represents about 1300 circuits with automated switching devices. At this point, most of the overhead circuits that would benefit significantly from automation are already automated, and the program is primarily focused on underground circuits.

For AMSR, there is a limited ability to address the reliability of overhead circuits. When a primarily overhead circuit is addressed in the WCR process, typical recommendations for reliability improvement include lateral fusing, FCIs, automation, and automatic reclosers. It is the plan of AMSR to include in-spection and maintenance work in its work scope for 2009 and beyond, which will increase its ability to address the reliability of overhead circuits. The reliability of underground circuits for SCE is primarily a function of equipment failures, especially cable failures. In addition, cable failure will increase as cables age, causing reliability to gradually be-come worse over time. SCE does not have specific CPUC requirements with regards to cable inspection and replacement, and therefore has a large amount of control with respect to cost and reliability for its underground distribution circuits. It is more difficult to improve the reliability of underground systems due to logistical factors such as the difficulty of obtaining new locations for new equipment. Therefore, capital reliability improvement op-tions are generally limited to cable replacement and the automation of existing switches. With regards to these options, the following observations are made:

Potential Capital Project to Improve Underground Reliability

- More automation. As mentioned previously, SCE has a separate group in charge of the deploy-ment of automated distribution switches. This group coordinates switch placement with the AMSR group. In addition, WCR recommendations may include automation, which are coordi-nated with the SCE automation function.

- Proactive cable replacement. SCE has a large amount of old distribution cable in service, and this cable will start to fail with increasing frequency. This will lead to worsening reliability indi-ces and increased operational costs. Proactive cable replacement can help with this situation, but should only be done if the right cable is replaced and the right amount of cable is replaced. This issued is discussed in detail in Section 5.


AMSR has three primary spending categories: proactive cable replacement, sub-surface switch replace-ment, and WCR. Each of these three categories is now discussed briefly: Switch Replacement The present sub-surface switch replacement program is justifiable based on risk and safety considerations. Worst Circuit Rehabilitation (WCR) WCR spending is justified as long as (1) the identified circuits are appropriate, and (2) the projects rec-ommended are cost effective. The first criterion is satisfied by AMSR and has previously been discussed in Section 3. The second criterion is treated qualitatively by AMSR by having designers work with field operations to identify a scope of work for a WCR circuit that is cost effective based on local knowledge and experience. This process has an opportunity for improvement through the use of predictive reliability modeling. A predictive reliability model is able to compute the reliability characteristics of a circuit based on system topology, device locations, and operational characteristics. This allows the reliability benefits of potential projects to be computed before they are actually performed. By using predictive reliability models, SCE could identify the appropriate amount of investment for each WCR circuit based on either achieving ac-ceptable reliability or reaching a point where the benefit-to-cost ratio of additional projects are not ac-ceptable. In addition, predictive reliability models can help to identify the combination of reliability pro-jects that is able to achieve reliability benefits for the least possible cost. SCE is presently undertaking a pilot study in partnership with a consultant to create predictive reliability models for 15 circuits. This is an opportunity to begin the integration of predictive reliability modeling into their processes in an appropriate manner. When pursuing predictive reliability modeling, the benefits of improved incremental decision making must always be balanced against the cost associated with the modeling and analysis process. Proactive Cable Replacement AMSR is presently identifying cable sections for proactive replacement, which is required in order to ef-fectively manage the aging cable population. Proactive cable replacement is a problematic issue for many utilities around the county since there is commonly an aversion to replacing functioning in-service equip-ment. However, not pursuing proactive cable replacement will almost certainly lead to unacceptable fu-ture system reliability and unacceptable numbers of cable failures that are difficult to address with exist-ing resources. The AMSR proactive cable replacement activities are described in the next section.


5 Proactive Cable Replacement

The primary issue facing AMSR is proactive cable replacement. This is because (1) aging cables are start-ing to result in lower levels of system reliability, (2) there is often an aversion by interveners and regula-tors to replace equipment that is in service and functioning, and (3) proactive cable replacement requires significantly higher levels of spending than historical levels. To be successful in recommending proactive cable replacement, any regulated utility must have compelling answers to the following questions: Important Questions to Answer with Regards to Proactive Cable Replacement

- Why should we proactively replace cable? - Why should we start now? Should we have started sooner? Can we defer this for a few years? - Which cables should be replaced first? Do we know the location of these cables? - How much proactive replacement is appropriate?

AMSR is able to sufficiently answer all of these important questions. This is with regards to proactive cable replacement considering the current amount of proposed replacement each year. The remainder of this section elaborates on this finding. The first step in proactive cable replacement is to examine the reliability of cable as it ages. These failure rate models can then be used to project system reliability into the future for a variety of proactive re-placement scenarios. AMSR has documented both its cable failure rate models and system reliability models in report AMSR-07-01 dated April 11, 2007. This report goes far beyond the efforts of most utili-ties, and can be considered best-in-class. A summary of these models is now provided. AMSR is able to identify the amount of installed cable per year from ledger accounts in the CARS data-base. AMSR is also able to compute the difference in ledger accounts from year to year. With this infor-mation, AMSR is able to compute failure rates for cable installed in a specific year. To do this, account changes from 2002 through 2006 are used (five years). For example, consider cable installed in 1980. As-sume there are 1000 conductor miles of 1980 cable in the ground at the beginning of 2002. At the end of 2006, there are only 800 conductor miles of 1980 cable left in the ground. This corresponds to 200 miles of replacement over five years, or 40 miles per year on average. This calculates to an average of 40 / 1000 = 4% replacement per year (these numbers are illustrative). As previously discussed, each ledger account combines both primary and secondary cable; AMSR assumes that half of the cable in each account is primary and that the other half is secondary. AMSR has computed the replacement rate for all cable ages dating back to 1957 using cable replacement data from 2002 through 2006. Since each year corresponds to a specific cable type (for the most part), failure rates for different cable ages are available and can be used to create unreliability functions (unreli-ability is the percentage of cable that has failed at a certain age). These functions are shown in Figure 5-1.


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Figure 5-1. SCE Cable Unreliability Models

The number of data points for different cable types is: PILC: 11 data points HMW: 3 data points XLPE: 28 data points TR-XLPE: 8 data points There are enough data points to create useful failure rate functions for both PILC and XLPE, which are the primary sources of cable failures on the SCE distribution system. HMW only has three data points, which is fewer than desirable when developing a failure rate model. However, there is only a small amount of installed HMW cable on the SCE system and the failure rate of HMW cable is relatively low. Therefore, it is not imperative to have high confidence in the HMW failure rate model. TR-XLPE has eight data points, but these only correspond to cables less than eight years in age. There-fore, it is not possible to project end-of-failure life from these historical data points and assumptions are required. In addition, failures of new TR-XLPE are dominated by a combination of infant mortality (where very new equipment tend to fail more often that slightly older equipment) and non-age-related failure such as dig-ins. This type of situation is true for all new classes of equipment and is unavoidable. However, this limitation will not impact short-term system reliability predictions since these predictions


are dominated by failures of other types of equipment. Predictions past 15 or 20 years in the future have a high percentage of TR-XLPE and will be highly dependent on the assumptions made for TR-XLPE end-of-life failure characteristics. System modeling requires age-versus-failure rate models. To do this, it is necessary to convert the unreli-ability models of Figure 5-1 to failure rate models. The CARS database can directly compute the total amount of replaced cable per year. The ODRM database contains the total number of cable failures per year, allowing the average number of replacement miles per failure to be computed. Presently, there is only slightly more than one year of ODRM data available. Therefore, AMSR is supplementing this data with four years of data from DTOM. Since DTOM does not capture area outages, AMSR is scaling up the DTOM outages based on the ratio of circuit to area outages as computed by ODRM. AMSR will eventu-ally shift to the exclusive use of ODRM data. The AMSR age versus failure rate models have been compared to models developed by Pacific Gas & Electric as shown in Chapter 18 of their 2007 General Rate Case filing (Electric Distribution Aging Infra-structure). The exhibit of this chapter describes the development of failure rate models for both PILC and XLPE cable based on age-specific cable data. These models are generally similar to the models developed by AMSR. AMSR has projected the contribution of cable failure to system reliability for a range of proactive cable replacement scenarios. This model sequentially simulates years by determining the number of cable fail-ures of each vintage and replacing failed cable sections with new TR-XLPE cable. In addition, proactive cable replacement is performed by always replacing the cable with the highest failure rate first. Scenarios have been examined for proactive cable replacement levels ranging from no proactive replacement to 800 miles per year. Results for the contribution of cable failures to SAIDI (in minutes) are shown in Figure 5-2, with each simulation starting in 2006 and ending in 2031.

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Figure 5-2. SCE Projections for Cable Failure Contributions to SAIDI


The top line Figure 5-2 shows the projected increase in SAIDI with no proactive cable replacement. Com-pared to 2006 levels, cable failures will approximately double by 2013, triple by 2018, and quadruple by 2031. Although this may seem high, it is lower than the projections estimated by PG&E in its 2007 Gen-eral Rate Case Filing. The 2006 SAIDI for SCE was 116 minutes (excluding major events), with cable failures (due to bad ca-ble) contributing about 18 minutes to this total. In absolute terms, reliability will increase from about 116 minutes in 2006 to about 170 minutes in 2031 if no proactive cable replacement is performed. This as-sessment assumes 3% annual system growth while all other reliability factors over this time period remain unchanged. A SAIDI increase of 47% over the next 25 years due to increasing cable failures is not an acceptable sce-nario. Even extensive automation deployment on an additional 2000 circuits would only improve SAIDI by about 10%. Clearly the proactive cable replacement activities of AMSR are needed by SCE. SCE has about 46,000 conductor miles of cable. Assuming that the expected life of cable is about 35 years (a conservative assumption), this means that eventually SCE will have to replace about 1300 miles of cable per year (total, including reactive replacement). SCE is has only been proactively replacing about 30 miles per year in recent years, and plans to increase this amount to the following: Table 5-1. Projected Levels of Proactive Cable Replacement

Year Miles

2006 30 (actual) 2007 50 2008 100 2009 250

The short-term SAIDI benefits of modest proactive replacement are small when compared to no proactive replacement. Consider the difference in the top two lines in Figure 5-2, which represent the difference between no proactive replacement and 100 miles per year. In 2011, 100 miles per year of proactive re-placement will improve SAIDI by about 2 minutes, which is not noticeable considering natural variations in SAIDI from year to year. Even aggressive replacement of 800 miles per year will only result in about a 7 minute SAIDI improvement in 2011. However, managing a large system of aging cable over its useful life requires looking out more than five years. In the case of SCE, cable failure rates start to increase to modest levels in 2011 and aggressively begin to increase beyond this point. As mentioned previously, SCE will eventually have to be replacing about 1300 conductor miles of cable per year. Presently, about 300 miles per year is being replaced due to cable failures, and about an additional 50-250 miles per year is planned for proactive replacement over the next few years. This amount is appropriate in the short-term, but will need to be significantly in-creased over time. AMSR estimates that they able to identify over 3000 miles of specific proactive cable replacement pro-jects that can be justified by the current data and analyses. This is done though a combination of the WCR program and by locating older cable sections through FIMs (with the possibility of being aided by vault age data from Passport and automatic data extraction). Since the levels of proposed proactive replacement


is well below this number, the proactive cable projects identified by AMSR for at least the next five years can be considered valid. As the most obvious proactive cable replacement projects become completed over time and as the level of proactive cable replacement increases, AMSR will benefit from the ability to be more detailed in both its cable failure rate models and in its identified proactive cable replacement projects. The processes are more than acceptable today, but suggestions for the future include:

1. When computing replacement miles, remove all projects not associated with age-related failures.

For example, any cable removed as part of a circuit upgrade or relocation should not be included when computing replacement miles. Similarly, cable replaced due to a dig-in should not be in-cluded.

2. Attempt to identify the specific percentage of primary and secondary cable in each CARS ac-count.

3. Be more precise when converting replacement miles to failures. For example, typical PILC fail-ures may results in a different amount of replacement miles than XLPE failures. If AMSR can be more precise when performing these calculations, higher confidence can be given to the failure rate models.

4. Include additional features in cable failure models. For example, there may be differences in ca-ble of different voltage classes or insulation thickness. There may be differences in main trunk versus lateral characteristics. There may be different failure characteristics for different geo-graphic areas. If AMSR can develop more detailed models, more targeted replacements programs can be designed.

It is possible that the above four suggestions could be implemented by using data mined from the DPI database. AMSR has advanced cable failure rate models and aging system reliability models when compared to the industry as a whole. The resulting predictions have the opportunity to become more specific over time with better data and better analytical models, but the current predictions for escalating cable failures and the corresponding impact to system reliability are able to identify appropriate levels of cable replacement and appropriate replacement projects.


6 Conclusions and Recommendations

The Asset Management and System Reliability group at Southern California Edison (AMSR) has three categories of spending: the replacement of subsurface switches, worst circuit rehabilitation, and proactive cable replacement. In each of these areas, AMSR is making appropriate spending decisions as bench-marked against the requirements of asset management, the overall objectives of Southern California Edi-son, and the approaches of other large investor-owned utilities in the United States. Although AMSR benchmarks highly against similar activities at other utilities, there is always room for improvement in the emerging area of aging infrastructure management. A summary of recommendations resulting from the InfraSource review of AMSR are now presented. These recommendations have not been investigated with regards to cost and feasibility, and should therefore be considered a starting point for further investigation:

Recommendations

1. AMSR should consider increased staffing. 2. AMSR should consider adding some inspection and maintenance budget responsibility to its capi-

tal budget responsibility. This can then be used for WCR reliability improvement and cable in-spection activities. AMSR is in the process of requesting this expansion of its responsibilities.

3. When inspection and maintenance dollars become available to AMSR, it should consider transi-tioning towards the use life-cycle costing so that one-time capital dollars can be appropriately compared to recurring inspection and maintenance dollars.

4. AMSR should consider using predictive reliability modeling to compute the reliability benefits of potential projects. This is especially desirable for WCR analyses. AMSR is in the process of de-veloping this capability.

The biggest issue facing AMSR is aging underground cable. Southern California Edison will have to sig-nificantly increase the amount of proactive cable replacement in the near future to avoid significant wors-ening of system reliability. For higher levels of cable replacement that will be seen in five years and be-yond, it will become increasingly desirable to base decisions on models that go beyond just the year of installation as the salient feature. Several specific suggestions for AMSR are provided in the body of the report (see Section 5). The data, methods, and results of AMSR for proactive equipment replacement are of higher quality than most other large utilities. In addition, the organization structure of AMSR within SCE makes it more ef-fective in making proactive equipment replacement decisions when compared to many other utilities. The data and methodologies used by AMSR are appropriate and have resulted in reliability predictions with regards to increasing cable failures, the impact of increasing cable failures on system reliability, and the impact of proactive cable replacement on future reliability. The approach of AMSR for proactive equipment replacement is more sophisticated and produces results with higher confidence when compared to most other large utilities. Spending requests by AMSR are reasonable, higher spending request levels could be justified at this time, and higher levels of spending on proactive equipment replacement will be required in the future.


Appendix A: Qualifications of Richard E. Brown

Summary of Qualifications

Richard Brown is the Vice President of Distribution for the Technology Division of InfraSource. He is a recognized international expert in power system reliability and asset management. Dr. Brown has published more than 80 tech-nical papers and articles in these areas, is author of the book Electric Power Distribution Reliability, and is author of the reliability chapter in the Electric Power Engineering Handbook. Dr. Brown is an IEEE Fellow and a registered professional engineer.

Education

Degree Institution Location Year M.B.A. University of North Carolina Chapel Hill, NC 2003 Ph.D. University of Washington Seattle, WA 1996 M.S.E.E. University of Washington Seattle, WA 1993 B.S.E.E. University of Washington Seattle, WA 1991

Professional Experience

Title Institution Dates Vice President of Distribution InfraSource Services, Inc. 7/2006 - present Senior Principal Consultant KEMA Inc. 5/2003 - 6/2006 Director of Technology ABB Consulting 5/2001 - 4/2003 Principal Engineer ABB Power Distribution Solutions 2/1999 - 4/2001 Senior Engineer ABB Corporate Research 7/1996 - 1/1999 Research/Teaching Assistant University of Washington 1/1994 - 6/1996 Electrical Engineer III Jacobs Engineering 1/1993 - 12/1993 Electrical Engineer II Jacobs Engineering 4/1991 - 12/1992

Professional Registration and Professional Societies

• IEEE Power Engineering Society - Elected Fellow in 2006 [this membership grade is conferred by the IEEE Board of Directors upon a per-son with an extraordinary record of industry accomplishments]

- Member, Power System Planning and Implementation Committee (1997-present) - Vice Chair (2006 - present) - Chair, Distribution Working Group (2003-2006) - Chair, Reliability Working Group (1997-1999)

- Member, Working Group on Distribution System Design (1997-present) - President, University of Washington Student Chapter (1994-1995) - Vice President, University of Washington Student Chapter (1993-1994) - Technical Paper Reviewer for IEEE Transactions (1996-present)

• Registered Professional Engineer in the State of North Carolina (Certificate No. 23088)

Honors and Awards

• IEEE PES Walter Fee Outstanding Young Engineer Award (2003)

• Listed in Marquis Who’s Who in America

• Listed in Marquis Who’s Who in Science and Engineering

• Listed in Madison’s Who’s Who

• ABB Award of Excellence: President’s Award (1999), Product Development (1998)

• Member: Eta Kappa Nu (Electrical Engineering Honor Society)

• Member: Beta Gamma Sigma (Business Honor Society)


IEEE Power Engineering Society Activities

• Elected IEEE Fellow in 2007 for “contributions to distribution system reliability and risk assessment.” The grade of Fellow is conferred by the IEEE Board of Directors for an extraordinary record of industry ac-complishments.

• Awards - Technical Committee Working Group Recognition Award (2006). For work which resulted in a special

issue of the IEEE Power and Energy magazine, May 2005.

- Walter Fee Outstanding Young Engineer Award (2003). For outstanding contributions in predictive reli-ability modeling of distribution systems.

• Vice President, University of Washington Student Chapter (1993-1994)

• President, University of Washington Student Chapter (1994-1995)

• Member, Power System Planning and Implementation Committee (1997-present) - Committee Vice Chair (2006-present) - Chair, Distribution Working Group (2003-2006)

- Chair, Power Delivery Reliability Working Group (1997-1999)

• Member, Distribution Subcommittee, Working Group on System Design (1997-present)

• Technical Paper Reviewer - IEEE Transactions on Power Systems (1996-present)

- IEEE Transactions on Power Delivery (1996-present) - IEEE General Meeting (2001-present) - IEEE T&D Conference and Exposition (2001-present) - IEEE Power Systems Conference and Exposition (2004-present)

Professional Experience

7/06 – present InfraSource Services, Raleigh, NC

Vice President — I am a founding principal of the Technology Division, which provides exper-tise, consulting, studies, training, advice, and guidance to electric utilities and industrial customers in the areas of power delivery expansion planning, asset management, reliability improvement, cost re-duction, risk management, and field resource management. I serve on the leadership team, provide value-added advice to customers, and manage all aspects of the distribution technology group.

5/03 – 6/06 KEMA T&D Consulting, Raleigh, NC

Senior Principal Consultant—As a charter member of the T&D Consulting (TDC) division in the US, I provided management and technical consulting services in the areas of reliability and as-set management. I also served on the US TDC leadership team, led the asset management group, and was responsible for strategic planning and business development. I played a key role in grow-ing the group to a $6 million per year business with cumulative positive cash flow within 18 months. I have also personally grew the asset management team from zero to $2 million within two years.

7/96 – 4/03 ABB

5/01 – 4/03 Director of Technology, ABB Consulting, Raleigh, NC ABB Consulting provides technical advice and training for both internal and external customers.

As Director of Technology, I had the responsibility for research and development of algorithms and tools to support existing capabilities and create new opportunities. In addition, I had the re-sponsibility for developing an eConsulting platform capable of providing comprehensive consult-ing services through an application service provider paradigm (e.g., system modeling, data ware-housing, knowledge bases, on-line training). Although this position is within the US Consulting


business of ABB, its scope was to provide global support for the entire $500 million Utility Part-ners business area, with a budget responsibility of $2.5 million.

2/99 – 4/01 Principal Engineer, ABB Power Distribution Solutions, Raleigh, NC

Power Distribution Solutions was created by ABB to provide customers with complete solutions based on functional requirements including design, build, own, operate, maintain, guarantee, and finance. I was selected to be a charter member of this group responsible for design expertise, op-timization expertise, and software expertise. Major accomplishments include:

- Line manager for a small team of highly skilled R&D engineers. - Development of Performance AdvantageTM, ABBs internal distribution system analysis and

optimization tool. This tool is capable of optimizing all aspects of distribution systems including electrical performance, reliability, economics, and risk. I was awarded the ABB Award of Ex-cellence (Product Development) for this effort.

- Development of Strategic AdvantageTM, ABBs internal spatial load forecasting and substation planning tool.

- Played a key role in securing a $127 million solution sale to Commonwealth Edison after their reliability problems in the summer of 1999. The capture team of seven people was awarded the ABB Award of Excellence (President’s Award) for this effort.

- Served as a consultant for the following electric utilities: Commonwealth Edison, Carolina Power and Light, TXU, NStar, Scottish Power, PacifiCorp, Florida Power & Light.

- Served as a consultant for the following C&I customers: General Motors, Ford, Monsanto, Armco Steel, Mobil Oil, Chevron.

- Instructor for numerous external and internal workshops in the areas of engineering, planning, reliability, and design optimization.

7/96 - 1/99 Senior Engineer, ABB Corporate Research, Raleigh, NC

ABBs R&D facility in the United States (formerly called The Electric Systems Technology Insti-tute) is located on the Centennial Campus of North Carolina State University. My job responsibili-ties included research, product development, consulting, project management, business develop-ment, and teaching workshops. Major accomplishments include:

- Created distribution system reliability assessment software sold commercially as ReliNETTM.

- Created substation reliability assessment software (SUBREL) distributed to ABB substation groups globally.

- Created budget constrained planning software that used marginal cost/benefit methods to opti-mally allocate utility capital and O&M budgets.

- Served as project manager for a $610,000 corporate research project that developed the tools and expertise necessary for ABB to transition from an equipment provider to a solution pro-vider.

- Provided consulting services for the following electric utilities: Duke Energy, Midwest Energy, Ameren, Meralco (Philippines), AEP, GPU, Florida Power and Light, Georgia Power, Baltimore Gas and Electric, PECO.

1/94 - 6/96 UNIVERSITY OF WASHINGTON, Seattle, WA Research/Teaching Assistant—My research done at the University of Washington was in the

area of distribution system reliability assessment and design optimization. Research was funded by Snohomish County PUD #1 on 2 successive contracts totaling $170,000. This project resulted in a distribution system reliability assessment software package (DS-RADS) which was later sold to Power Technologies, Inc. and is now a commercially available product. In addition to research, I served as a teaching assistant for various power systems and controls courses at the undergraduate and graduate level.


4/91 – 12/93 JACOBS ENGINEERING, Kirkland, WA

Engineer II and Engineer III—Jacobs Engineering (formerly Sverdrup Corp.) is a multi-disciplinary engineering firm with electrical, mechanical, civil, and structural design capabilities. Responsibilities included engineering design of medium voltage and low voltage electrical sys-tems for industrial facilities, institutional facilities, and public works. Typical work included de-sign, value engineering, specification writing, construction document generation, and construction support. Major projects included: - University of Florida (Orlando) Biotechnology Research and Development Facility: Lead elec-trical engineer including underground service, main switchgear, MCCs, emergency generation, life safety system, exterior lighting and interior lighting.

- Boeing Headquarters: Electrical systems design for an office park main substation, central plant, communications building, and underground site distribution system. Duties included de-sign of a 115-kV, 25-MVA substation, protective relaying, 15-kV, 5-kV, and 600-V distribu-tion, and fire detection/alarm systems. Performed an energy conservation study funded by Puget Sound Energy.

- Boeing Research Aerodynamic Icing Tunnel: Electrical systems design for a new equipment building, substation expansion, and wind tunnel structure. Duties included 600-V distribution system design and cost estimation for demolition and construction.

- Boeing Research Hot Gas Test Facility: Electrical systems design for a three-cell hot gas test facility. Duties included grounding system design, 600-V distribution system design, and heat trace system design.

- Arizona DOT SR-360 Traffic Interchange: Electrical systems design for an outer highway loop including two tunnels. Duties included design of staged interior HID tunnel lighting, 600-V power distribution, signal reference grid, and control room.

Books, Book Chapters, and Theses

1. R. E. Brown, Electric Power Distribution Reliability, Marcel Dekker, 2002.

2. R. E. Brown, author of the chapter “Distribution System Reliability: Analytical and Empirical Techniques” in IEEE Tutorial on Electric Delivery System Reliability Evaluation, J. Mitra (Editor), IEEE, 2005, pp. 39-51.

3. R. E. Brown, author of chapter “Power System Reliability” in Electric Power Engineering Handbook, L. L. Grigsby (EIC), CRC Press LLC, 2001, pp. 13-51 through 13-65.

4. R. E. Brown, Reliability Assessment and Design Optimization for Electric Power Distribution Systems, Ph.D. Dissertation, University of Washington, Seattle, WA, 1996.

5. R. E. Brown, An Intelligent Overload Relay for Extruded Dielectric Transmission Cable, Masters Thesis, Uni-versity of Washington, Seattle, WA, 1993.

Refereed Journal Papers

1. R. E. Brown, M. V. Engel, and J. H. Spare, “Making Sense of Worst Performing Feeders”, IEEE Transactions

on Power Systems, Vol. 20, No. 2, May 2005, pp. 1173-1178. 2. R. E. Brown, G. Frimpong, and H. L. Willis, “Failure Rate Modeling Using Equipment Inspection Data”, IEEE

Transactions on Power Systems, Vol. 19, No. 2, May 2004, pp. 782-787. 3. S. S. Venkata, A. Pahwa, R. E. Brown, and R. D. Christie, “What Future Distribution Engineers Need to

Learn,” IEEE Transactions on Power Systems, Vol. 19, No. 1, Feb. 2004, pp. 17-23. 4. F. Li and R. E. Brown, “A Cost-Effective Approach of Prioritizing Distribution Maintenance Based on System

Reliability,” IEEE Transactions on Power Delivery, Vol. 19, No. 1 , Jan. 2004, pp. 439-441. 5. F. Li, R. E. Brown, and L. A. A. Freeman, “A Linear Contribution Factor Model of Distribution Reliability

Indices and its Applications in Monte Carlo Simulation and Sensitivity Analysis,” IEEE Transactions on Power Systems, Vol. 18, No. 3, Aug. 2003, pp. 1213-1215.


6. R. E. Brown and A. P. Hanson, “Impact of Two Stage Service Restoration on Distribution Reliability,” IEEE Transactions on Power Systems, Vol. 16, No. 4, Nov. 2001, pp. 624-629.

7. R. E. Brown and J. J. Burke, “Managing the Risk of Performance Based Rates,” IEEE Transactions on Power Systems, Vol. 15, No. 2, May 2000, pp. 893-898.

8. R. E. Brown and M. M. Marshall, “Budget Constrained Planning to Optimize Power System Reliability,” IEEE Transactions on Power Systems, Vol. 15, No. 2, May 2000, pp. 887-892.

9. R. E. Brown, “The Impact of Heuristic Initialization on Distribution System Reliability Optimization,” Interna-tional Journal of Engineering Intelligent Systems for Electrical Engineering and Communications, Vol. 8, No. 1, March 2000, pp. 45-52.

10. R. E. Brown and J. R. Ochoa, “Impact of Sub-Cycle Transfer Switches on Distribution System Reliability,” IEEE Transactions on Power Systems, Vol. 15, No. 1, Feb. 2000, pp. 442-447.

11. R. E. Brown, T. M. Taylor, “Modeling the Impact of Substations on Distribution Reliability,” IEEE Transac-tions on Power Systems, Vol. 14, No. 1, Feb. 1999, pp. 349-354.

12. R. E. Brown and J. R. Ochoa, “Distribution System Reliability: Default Data and Model Validation,” IEEE Transactions on Power Systems, Vol. 13, No. 2, May 1998, pp. 704-709.

13. R. E. Brown, S. Gupta, R. D. Christie, S. S. Venkata, and R. D. Fletcher, “Distribution System Reliability: Momentary Interruptions and Storms,” IEEE Transactions on Power Delivery, Vol. 12, No. 4, October 1997, pp. 1569-1575.

14. R. E. Brown, S. Gupta, R. D. Christie, S. S. Venkata, and R. D. Fletcher, “Automated Primary Distribution System Design: Reliability and Cost Optimization,” IEEE Transactions on Power Delivery, Vol. 12, No. 2, April 1997, pp. 1017-1022.

15. R. E. Brown, S. Gupta, R. D. Christie, S. S. Venkata, and R. D. Fletcher, “Distribution System Reliability Analysis Using Hierarchical Markov Modeling,” IEEE Transactions on Power Delivery, Vol. 11, No. 4, Oct. 1996, pp. 1929-1934.

16. V. N. Chuvychin, N. S. Gurov, S. S. Venkata, and R. E. Brown, “An Adaptive Approach to Load Shedding and Spinning Reserve Control During Underfrequency Conditions,” IEEE Transactions on Power Systems, Vol. 11, No. 4, Nov. 1996, pp. 1805-1810.

Refereed Conference Papers

1. R. E. Brown, “Reliability Benefits of Distributed Generation on Heavily Loaded Feeders”, IEEE PES 2007

General Meeting, Tampa, FL, June 2007. 2. R. E. Brown, “Pole Hardening Following Hurricane Wilma,” 2007 Southeastern Utility Pole Conference, Tu-

nica, MS, Feb. 2007. 3. B. Ramanathan, D. Hennessy and R. E. Brown, “Decision-making and Policy Implications of Performance-

based Regulation,” IEEE Power Systems Conference and Exhibition, Atlanta, GA, Oct. 2006. 4. R. E. Brown, “The Regulatory Usefulness of Reliability Reporting,” 2006 IEEE Rural Electric Power Confer-

ence, Albuquerque, NM, April 2006. 5. M. Butts, H. H. Spare and R. E. Brown, “Practical and Verifiable Reliability Improvement at the Baltimore Gas

and Electric Company,” DistribuTECH Conference and Exhibition, Tampa Bay, FL, Feb. 2006. 6. R. E. Brown, “Project Selection with Multiple Performance Objectives,” 2005 IEEE/PES Transmission and

Distribution Conference and Exposition, New Orleans, LA, Sept. 2005. 7. R. E. Brown and J. H. Spare, “The Effects of System Design on Reliability and Risk,” 2005 IEEE/PES Trans-

mission and Distribution Conference and Exposition, New Orleans, LA, Sept. 2005. 8. R. E. Brown and J. H. Spare “A Survey of U.S. Reliability Reporting Processes,” 2005 IEEE/PES Transmis-

sion and Distribution Conference and Exposition, New Orleans, LA, Sept. 2005. 9. Y. Zhou and R. E. Brown, “A Practical Method for Cable Failure Rate Modeling,” 2005 IEEE/PES Transmis-

sion and Distribution Conference and Exposition, New Orleans, LA, Sept. 2005. 10. R. E. Brown and J. H. Spare, “Asset Management and Financial Risk,” DistribuTECH Conference and Exhibi-

tion, San Diego, CA, Jan. 2005. 11. R. E. Brown and J. H. Spare, “Asset Management, Risk, and Distribution System Planning,” IEEE Power Sys-

tems Conference and Exhibition, New York, NY, Oct. 2004.


12. R. E. Brown, “Identifying Worst Performing Feeders,” Probabilistic Methods Applied to Power Systems,

PMAPS 2004, Ames, IA, September 2004. 13. H. L. Willis, M. V. Engel and R. E. Brown, “Equipment Demographics – Failure Analysis of Aging T&D In-

frastructures,” 2004 Canada Power Conference, Toronto, Canada, September 2004. 14. R. E. Brown, “Failure Rate Modeling Using Equipment Inspection Data”, IEEE PES 2004 General Meeting,

Denver, CO, June 2004. 15. R. E. Brown, “Coming to Grips with Distribution Asset Management,” 2003 Real World Conference: It’s All

About Cost and Reliability, Transmission and Distribution World, Ft. Lauderdale, FL, Oct. 2003. 16. R. E. Brown, “Reliability Standards and Customer Satisfaction,” 2003 IEEE/PES Transmission and Distribu-

tion Conference and Exposition, Dallas, TX, Sept. 2003. 17. A. Pahwa, S. Gupta, Y. Zhou, R. E. Brown, and S. Das, “Data Selection To Train A Fuzzy Model For Over-

head Distribution Feeders Failure Rates," International Conference on Intelligent Systems Applications to

Power Systems, Lemnos, Greece, Sept. 2003. 18. R. E. Brown, “Network Reconfiguration for Improving Reliability in Distribution Systems,” IEEE PES 2003

General Meeting, Toronto, Canada, July 2003. 19. R. E. Brown, , J. Pan, Y. Liao, and X. Feng, “An Application of Genetic Algorithms to Integrated System Ex-

pansion Optimization,” IEEE PES 2003 General Meeting, Toronto, Canada, July 2003. 20. R. E. Brown and L. A. A. Freeman, “A Cost/Benefit Comparison of Reliability Improvement Strategies,” Dis-

tribuTECH Conference and Exhibition, Las Vegas, NV, Feb. 2003. 21. S. Gupta, A. Pahwa, R. E. Brown and S. Das, “A Fuzzy Model for Overhead Distribution Feeders Failure

Rates,” NAPS 2002: 34th Annual North American Power Symposium, Tempe, AZ, Oct. 2002.

22. R. E. Brown, “Web-Based Distribution System Planning,” IEEE PES Summer Power Meeting, Chicago, IL, July 2002.

23. R. E. Brown, “System Reliability and Power Quality: Performance-Based Rates and Guarantees,” IEEE PES Summer Power Meeting, Chicago, IL, July 2002.

24. R. E. Brown, “Modeling the Reliability Impact of Distributed Generation,” IEEE PES Summer Power Meeting, Chicago, IL, July 2002.

25. S. Gupta, A. Pahwa, R. E. Brown, “Data Needs for Reliability Assessment of Distribution Systems,” IEEE PES Summer Power Meeting, Chicago, IL, July 2002.

26. R. E. Brown, “Meeting Reliability Targets for Least Cost,” DistribuTECH Conference and Exhibition, Miami, FL, Feb. 2002.

27. S. Gupta, A. Pahwa and R. E. Brown, “Predicting the Failure Rates of Overhead Distribution Lines Using an Adaptive-Fuzzy Technique,” NAPS 2001: 33rd

Annual North American Power Symposium, College Station, TX, Oct. 2001.

28. P. R. Jones and R. E. Brown, “Advanced Modeling Techniques to Identify and Minimize the Risk of Aging Assets on Network Performance,” Utilities Asset Management 2001, London, UK, July 2001.

29. R. E. Brown, “Distribution Reliability Modeling at Commonwealth Edison,” 2001 IEEE/PES Transmission

and Distribution Conference and Exposition, Atlanta, GA, Oct. 2001. 30. R. E. Brown, “Distribution Reliability Assessment and Reconfiguration Optimization,” 2001 IEEE/PES

Transmission and Distribution Conference and Exposition, Atlanta, GA, Oct. 2001. 31. R. E. Brown, J. Pan, X. Feng and K. Koutlev, “Siting Distributed Generation to Defer T&D Expansion,” 2001

IEEE/PES Transmission and Distribution Conference and Exposition, Atlanta, GA, Oct. 2001. 32. D. Ross, L. Freeman and R. E. Brown, “Overcoming Data Problems in Predictive Distribution Reliability

Modeling,” 2001 IEEE/PES Transmission and Distribution Conference and Exposition, Atlanta, GA, Oct. 2001. 33. R. E. Brown and L. A. A. Freeman, “Analyzing the Reliability Impact of Distributed Generation,” IEEE PES

Summer Power Meeting, Vancouver, BC, Canada, July 2001. 34. R. E. Brown and M. Marshall, “Microeconomic Examination of Distribution Reliability Targets,” IEEE PES

Winter Power Meeting, Columbus, OH, Jan. 2001, Vol. 1, pp. 58-65. 35. P. R. Jones and R. E. Brown, “Investment Planning of Networks Using Advanced Modeling Techniques,”

Utilities Asset Management 2001, London, UK, Jan. 2001. 36. R. E. Brown, “Probabilistic Reliability and Risk Assessment of Electric Power Distribution Systems,” Distrib-

uTECH Conference and Exhibition, San Diego, CA, Feb. 2001. 37. C. LaPlace, D. Hart, R. E. Brown, W. Mangum, M. Tellarini, J. E. Saleeby, “Intelligent Feeder Monitoring to

Minimize Outages,” Power Quality 2000 Conference, Boston, MA, Oct. 2000.


38. R. E. Brown, H. Nguyen, J. J. Burke, “A Systematic and Cost Effecting Method to Improve Distribution Reli-ability,” IEEE PES Summer Meeting, Edmonton, AB, July 1999. Vol. 2, pp. 1037-1042.

39. R. E. Brown, T. M. Taylor, “Modeling the Impact of Substations on Distribution Reliability,” IEEE PES Win-

ter Meeting, New York, NY, Feb 1999, pp. 349-354. 40. R. E. Brown, A.P. Hanson, M.M Marshall, H.L. Willis, B. Newton, “Reliability and Capacity: A Spatial Load

Forecasting Method for a Performance Based Regulatory Environment,” 1999 Power Industry Computer Appli-

cations Conference, Dayton, OH, February 1999, pp. 139-144. 41. R. E. Brown, A. P. Hanson, D. Hagan, “Long Range Spatial Load Forecasting Using Non-Uniform Areas,”

1998 IEEE/PES Transmission and Distribution Conference, New Orleans, LA, April 1999, Vol. 1, pp. 369-373. 42. R. E. Brown, W. S. Zimmermann, P. P. Bambao Jr., and L. P. Simpao, “Basic Planning for a New Fast Grow-

ing Area in Manila with a Total Electrical Load of 650 MVA,” 12th Annual Conference of the Electric Power

Supply Industry, Pattaya, Tailand, November 1998. 43. X. Y. Chao, R. E. Brown, D. Slump, and C. Strong, “Reliability Benefits of Distributed Resources,” Power

Delivery International ‘97 Conference, Dallas, TX, December 1997. 44. R. E. Brown, “Competitive Distribution Systems: A Reliability Perspective,” American Power Conference,

Vol. 59-II, Chicago, IL, April 1997, pp. 1115-1120. 45. R. E. Brown, S. S. Venkata, and R. D. Christie, “Hybrid Reliability Optimization Methods for Electric Power

Distribution Systems,” International Conference on Intelligent Systems Applications to Power Systems, Seoul, Korea, IEEE, July 1997.

46. R. E. Brown, S. Gupta, R. D. Christie, S. S. Venkata, and R. D. Fletcher, “Automated Primary Distribution System Design: Reliability and Cost Optimization,” 1996 IEEE/PES Transmission and Distribution Confer-

ence, Los Angeles, CA, Sept., 1996, pp. 1-6. 47. R. E. Brown, S. S. Gupta, R. D. Christie, and S. S. Venkata, “A Genetic Algorithm for Reliable Distribution

System Design,” International Conference on Intelligent Systems Applications to Power Systems, Orlando, FL, January 1996, pp. 29-33.

Technical Articles

1. R. E. Brown, “Increased Performance Expectations for Major Storms,” Electric Perspectives, EEI (to be pub-lished in 2007)

2. E. Phillips, R. E. Brown, M. V. Engel, N. Bingel, “Transmission Pole Failures During High Winds,” Transmis-

sion and Distribution World, (to be published in May 2007) 3. R. E. Brown and D. J. Morrow, “The Challenge of Effective Transmission Planning,” IEEE Power and Energy

Magazine, (to be published in 2007). 4. R. E. Brown and H. L. Willis, “The Economics of Aging Infrastructure,” IEEE Power and Energy Magazine,

Vol. 4, No. 3, May/June 2006, pp. 36-43. 5. R. E. Brown and B. G. Humphrey, “Asset Management for Transmission and Distribution,” IEEE Power and

Energy Magazine, Vol. 3, No. 3, May/June 2005, pp. 39-45. 6. R. E. Brown, “Asset Management: Balancing Performance, Cost, and Risk,” EnergyPulse Special Issue on

Asset Management, www.energycentral.com, Feb. 2005. 7. P. Musser, R. E. Brown, T. Eyford, and C. Warren, “Too Many Routes of Reliability,” Transmission and Dis-

tribution World, June 2004, pp. 17-22. 8. T. M. Taylor, R. E. Brown, M. L. Chan, R. H. Fletcher, S. Larson, T. McDermott, and A. Pahwa, “Planning for

Effective Distribution,” IEEE Power and Energy Magazine, Vol. 1, No. 5, September/October 2003, pp. 54-62. 9. R. E. Brown and L. A. A. Freeman, “A Cost/Benefit Comparison of Reliability Improvement Strategies,” Elec-

tric Power and Light, May 2003. 10. R. E. Brown, H. Kazemzadeh, B. R. Williams and C. B. Mansfield, “Engineering Tools Move into Cyber-

space,” Transmission and Distribution World, March 2003, pp. 27-36. 11. F. Li, L. A. A. Freeman and R. E. Brown, “Web-Enabling Applications for Outsourced Computing,” IEEE

Power and Energy Magazine, Vol. 1, No. 1, January/February 2003, pp. 53-57. 12. P. Perani and R. E. Brown, “Maintaining Reliable Power For Semiconductor Manufacture,” What’s New in

Electronics, March 2002. 13. P. Perani and R. E. Brown, “Rock Steady: The Importance of Reliable Power Distribution in Microprocessor

Manufacturing Plants,” ABB Review, No. 3, 2002, pp. 29-33.


14. H. L. Willis and R. E. Brown, “Is DG Ready for the Last Mile?” Power Quality (cover story), March 2002. pp. 16-21.

15. R. E. Brown and M. W. Marshall, “The Cost of Reliability,” Transmission and Distribution World (cover

story), Dec. 2001, pp. 13-20. 16. R. E. Brown, P. R. Jones and S. Trotter, “Planning for Reliability,” Trans-Power Europe, Vol. 1, No. 1. March

2001, pp. 10-12. 17. R. E. Brown, A. P. Hanson, H. L. Willis, F. A. Luedtke, M. F. Born, “Assessing the Reliability of Distribution

Systems,” IEEE Computer Applications in Power, Vol. 14, No. 1, Jan. 2001, pp. 44-49. 18. R. E. Brown and B. Howe, “Optimal Deployment of Reliability Investments,” E-Source, Power Quality Series:

PQ-6, March 2000.

Documents

Asset Management and System Reliability Group …quanta-technology.com/.../files/doc-files/InfraSourceFinalReport.pdf · Final Report: Asset Management and System Reliability Group