20150812AVU to Staff 50 Attachment F.pdf

Author: Rodney Pickett
Company represented: Avista Utilities
Role: Asset Management Planning
Sector: Electric Distribution
Asset owner: Avista Utilities
Introduction
Avista Utilities implemented a new Electric Distribution Wood Pole Management (WPM) program in 2009
to better manage our assets. We revised our inspection and work cycle time from 100 years to 20 years and expanded the scope of the work to include wood poles, cross-arms, grounding, Distribution transformers,
wildlife guards, insulators, lightning arresters, and cutouts (combination of a fuse holder and an air switch
usually on a Distribution Transformer to protect it from an over-current condition and to isolate it). The
WPM program starts by inspecting every poles based on a plan to identify which components need to be
repaired or replaced. Based on the inspection results, we review each pole identified and develop a maintenance and replacement plan. We then repair or replace all of the components identified by the
inspection and design. This program along with some similar programs helped turned a reactive
maintenance strategy into a proactive strategy while maintaining system reliability.

Description of assets in study
We created the original WPM model based on the age profile shown Figure 1below based on a random
sampling of paper records for approximately 3,000 poles. Through several years of WPM work to also
inventory poles into an electronic database and other similar work, we developed a much better picture of our actual age profile shown in Figure 2.
The analysis covered the following:
244,000 Electric Distribution Wood Poles, Cross-arms, and sets of Insulators
118,000 Cutouts, Grounds, Wildlife Guards, Lightning Arresters and Distribution Transformers
Asset Planning Example – Wood
Pole Analysis
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Figure 1, Estimated Electric Distribution Wood Pole Age Profile from 2006
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Figure 2, Electric Distribution Wood Pole Age Profile for 2013
Based on re-evaluating the initial WPM inspection results in 2008, we expanded the program to include
Cutouts, Grounds, Wildlife Guards, Lightning Arresters and Distribution Transformers because of the
number of issues identified in the inspections in 2009. Most of the additional work focused on the Overhead Distribution Transformers which had the age profile shown in Figure 3 below.
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Electric Distribution Wood Pole Age Profile
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Figure 3, Electric Distribution Overhead Transformer Age Profile for 2010

When was the activity carried out?
We developed an RCMCost model using Availability Workbench from Isograph in late 2006. Based on this model, Avista planned for implementing a revised WPM program in 2008 focused on replacing and
repairing our wood structures that included poles and cross-arms. Later in 2008 as we examined the initial
inspection results, we decided to include Cutouts, Grounds, Wildlife Guards, Lightning Arresters and
Distribution Transformers in the program as well starting in 2009 since the amount of work exceeded what
the local offices could support. The program continues today and we continue to improve the original model used to justify the original program to ensure the program remains the best approach to maintaining wood
poles and their associated components.

Why was the activity carried out?
Prior to 2008, Avista inspected their wood poles on a 75 to 100 year cycle that depended on what they
budgeted for the wood pole inspections. However, we knew we could not maintain this cycle because we
thought wood poles would not last that long and we would have a major disaster when a major storm hit our
area.
Terminology
Customer Internal Rate of Return = Represents the internal rate of return our customers would realize
through rates if a program or project is implemented. This value is unique to regulated utilities because
Operations and Maintenance (O&M) costs and Capital cost are not treated equally in rates. SAIFI = System Average Interruption Frequency Index = Number of customers which had sustained outages
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Distribution Overhead Transformer Age Profile
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Total number of customers served

SAIDI = System Average Interruption Duration Index = Outage duration multiplied by the number of customers effected for all sustained outages
Total number of customers served
Sustained Outage = an outage lasting 5 minutes or more

Redundancy Factor = probability that a particular consequence will occur when a component fails,
corrective maintenance, planned maintenance, or an inspection is performed.
Description of activity

Methodology
We modelled three different alternatives, 100 year inspection cycle, 10 year inspection cycle, and 20 year
inspection cycles using the RCMCost module in Availability Workbench by Isograph. The process for
developing the model started with creating the failure curves based on Weibull equations. These failure
curves were created in Availability Workbench using the Weibull module for available data (see Figure 4). If no data existed, we used the input of our subject matter experts (SME) to input the different values used in a Weibull failure curve. Each failure curve or failure model was tied to a failure cause and asset. We built
our models on individual assets, so for example, a single pole had different causes assigned to it that
represented the different failure modes of the pole and its associated equipment.
For the lifecycle analysis, we selected a 50 year timeframe since this represent an approximation of all the model components average expected life.

As part of the model development, we had also developed an FMEA (Failure Mode and Effects Analysis)
that identified the function and functional failures of each component along with its associated causes (see Figure 5). As an example, a pole’s function is to support the distribution equipment with enough strength to survive a once in fifty year storm. Its functional failure is it no longer has sufficient strength to survive a
fifty year storm. Based on this functional failure definition, this failure mode is a hidden failure or Dormant
Failure until an inspection is performed or a once in fifty year storm hits.
Each failure curve supports a failure cause. The failure cause information includes a quick description of the cause and supporting information as shown in Figure 6.
Staff_PR_050 Attachment F Page 4 of 17
Figure 4, Weibull Failure Curve for Wood Poles
Poles Replaced Cumulative Probability
3.454E+04 9.731E+04 2.742E+05 7.726E+05
Time
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Eta estimator
P0: 0%
B50: 7.228E+05
B20: 5.744E+05
B10: 4.933E+05
ε: 0.0385
γ: 0
β: 4.93
η: 7.786Ε+05
Median rank
2-parameterWeibull
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Figure 5, Example of the Model Hierarchy: Location-Function-Functional Failure - Cause
Figure 6, Cause Description and General Information
We then assigned specific risks to corrective maintenance (CM), planned maintenance (PM), and
inspections along with an estimated redundancy factor for each cause (see Figure 7). These risks apply to
the specific cause and usually quantified in terms of money or risk threshold values. The probability for
determining risk is actually the function of two different values in the model. The first probability comes from the Weibull curve and provides the probability a pole would fail. The second probability is the
redundancy factor which represents the probability that the consequence would occur each time it fails.
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Figure 7, Entering Effects into the WPM Model
Next, we tie the appropriate failure curve to the cause used in the model as shown in Figure 8. We also add the current age of the component and other features to account for how the probability of failure changes over time.
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Figure 8, Linking the Failure Curve and Initial Age For CM and PM tasks, we assigned the specific labor, equipment, and material needed to correct the failure along with the task duration. Figure 9 shows the basic inputs for all activities using the Corrective
Maintenance activity. Specifically for the PM tasks, we assigned them as secondary tasks to the inspection,
so that they would only be initiated if the inspection identified a problem. For the inspections, we assigned
the labor and equipment required to perform the inspection along with its duration. We also specified a p-f interval associated with the inspection when appropriate to reflect how well an inspection can predict a future failure based on the current condition.

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Figure 9, Basic Data Input for Corrective Maintenance, Planned Maintenance, and Inspections
Once all of the cause blocks were developed and assigned to an individual component, we input the number
of components with identical ages (year of installation) until the model represented all 244,000 poles and all of the associated components. You can see an example of this in Figure 5, which also shows a location
hierarchy to organize the different assets and apply the appropriate multipliers.

While the WPM models were some of our first models, they now represent how we analyze our assets and
develop our Asset Management plans. This approach has several significant advantages. This method integrated a very significant amount of information to thoroughly analyze each of the three alternatives. By
comparing each models result to one another, you see the value of each alternative and their different
impacts to risk, cost, resources, and changes over time. Once you select the alternative, the models provide
you the needed budget estimates, labor estimate, equipment estimates, material estimates, metrics for
tracking performance over time, and significantly more information.
Since the original model for WPM, we monitor the progress of the work and our system’s performance and
have updated the WPM model three times in 7 years. While the initial model was very rough and based on a
very limited data set, it has proven effective in all subsequent models which have much more detailed
information than the original model.

Staff_PR_050 Attachment F Page 9 of 17
References
See the Availability Workbench user’s manual for the process and specific inputs and outputs.

Risk types
When the original WPM model was created, Avista had not yet developed our risk matrix. Since then, we
developed a corporate risk matrix and risk register for high level functions in the company. Based on the
corporate risk matrix, we then developed a subset risk matrix that feeds into the corporate risk matrix. We use the subset risk matrix on projects and programs which are generally much smaller than the corporate
level.

The current WPM models incorporate availability risk in terms of SAIFI and SAIDI, Financial risk,
Environmental Risk, Safety Risk, and Customer Outage Cost risks. Figure 7 shows an example of the effects for a wood pole.

Risk management process
Our current process includes specific risks and assigns a monetary value or risk thresholds to each risk in
most cases. Most of the specific risks are aligned with the corporate risk matrix. We generally use a subset
matrix with lower financial and lower risk thresholds. These subsets, allow us to identify risky projects or
situations earlier in the process, so that they remain below the corporate risk thresholds when individual
projects are bundled together at the corporate level.
Since the risk evaluation is inherent in our asset management model development and planning, we provide
a risk profile in our asset management plans. This profile is also provided to the corporate risk manager
once a plan has been finalized for their evaluation. An example of the risk summary information is shown
below in Figure 10.
Figure 10, Effects Summary Report
When we calculate the customer internal rate of return (CIRR), we include all risk based costs. For the
WPM program, risk costs are the main drivers for the 20 year inspection cycle. If risk costs were ignored, then we would have run the equipment to failure.

Staff_PR_050 Attachment F Page 10 of 17
Tools used
Availability Workbench version 2.1.19.0 by Isograph.
Excel 2007 by Microsoft (used for calculating the Customer Internal Rate of Return)

Costing
Since we don’t have a system to accurately capture CM and PM costs, our team of experts representing
engineering, supply chain, operations, and asset management worked to estimate the labor time, equipment time, and materials used for each CM and PM. Figure 11 shows an example of how labor and equipment
costs are entered into a model and for this model we chose to use a per hour rate for labor. Our inspection
costs come from our inspection contractor and are a per unit value, so they are well known. Based on the
estimates, we evaluated the overall project cost to ensure it made sense with what we had seen in the past.
We then applied an escalation factor to all costs to address future inflation.
Figure 11, Example of Labor Costs used in the model We then broke the costs into O&M and Capital costs and added the appropriate escalation factor to create
the lifecycle cost profile shown in Figure 12. This profile was then loaded into our financial tool to calculate
the CIRR as shown in Table 1 below. We treat risk costs as an O&M cost in our CIRR analysis, so we show
the CIRR without effects in the top portion of Table 1 and included the effects in the bottom portion of the
table. We developed this approach in the original model and our corporate officers endorsed the process. Table 1 shows the result from our 2012 model. You will notice that the 10 year Inspection cycle had a
slightly higher CIRR but because the different was so small, we elected to continue with a 20 year cycle to
avoid doubling our capital spending on the program.

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Table 1, Customer Internal Rate of Return Results for all Alternatives
Title
Effects
Excluded
Included
Included

Figure 12, 50 Year Cost Profile for Wood Pole Management

People
Our asset management teams represent a slice of our organization as much as practical. We included
managers, engineers, line foremen, safety, supply chain, asset management, and environmental in the initial models. With the tools we have developed since the original models, safety and environmental perspective
Cost Profile
Project
R.Total.OM
R.Total.Cap
R.Effects
0 43800 87600 1.314E+05 1.752E+05 2.19E+05 2.628E+05 3.066E+05 3.504E+05 3.942E+05 4.38E+05
Time
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are represented in the risk matrix and supply chain is brought into the process when needed. Once the Asset
Management Plan was completed, we presented it to our executives who approved the initial and
subsequence program changes.
Evaluation
What was the main output of the activity?
Avista identified an asset strategy to maintain our Electric Distribution system which included an optimized
defined scope of assets and work, an optimized inspection cycle, detailed budgets, resource plans, identified risk profile, and a justification for regulators for the additional expenses needed to fund the program. The budgets included the Operations and Maintenance (O&M) and Capital funding needed and showed how the
two budgets interact.

As for reliability, our goal was to maintain the current reliability performance of our system and check the increasing trend in the number of outage events.
Ultimately, we achieved our initial goal of changing how we manage our wood poles and related
components and created a sustainable approach.

Validation
Based on the model results and objectives, we established key performance indicators (KPI) and metrics to
monitor the performance of the program as shown in Table 2,Table 3, and Table 4 below. Both the KPI’s
and metrics came from the model developed above. We monitor these results annually to ensure we continue to receive the value from Wood Pole Management and identify when changes may need to be
implemented.

Table 2, Wood Pole Management Key Performance Indicators
KPI
Description
WPM Goal Related
number of OMT Events
Actual WPM
Related number
of OMT Events
Projected Miles
Follow-up Work
Actual Miles Follow-
up Work Completed
* These results were worse than anticipated

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Table 3, Wood Pole Management Metrics

Table 4, Wood Pole Management Metrics continued

* These results were worse than anticipated
Base on the results above, Table 2 shows we have not completed sufficient follow-up work for the plan. We
have used this information to adjust our Capital budget for the program and coordinated the work with
another related program to reduce our backlog of work. From Table 4, we see that the work we are
completing is using more material than anticipated. The inspection phase of WPM shows we under estimated the number of missing wildlife guards and issues with missing or failed lightning arresters (see
Figure 13).
Metric Description
Projected WPM Contribution To The Annual SAIFI Number
Actual WPM
Contribution To The Annual SAIFI Number
Projected Number of Dist Poles Inspected Actual Number of Dist Poles Inspected
2009 0.214024996 0.1863468 12600 13,161
2010 0.208489356 0.19916836 12600 15,553
2011 0.211022023 0.202462739 12600 13,324
2012 0.211022023 0.16613099 12600 17,318
2013 0.211022023 0.15640942 12600 14,364
2014 0.211022023 12600 2015 0.211022023 12600
Description Model Predicted
Material Use for WPM Follow-up Work
Material Use for WPM Follow-up
Projected
Number of Pole Rotten OMT Events
Actual
Number of Pole Rotten OMT Events
Projected
Number of Crossarm OMT Events
Actual Number of Crossarm OMT Events
2009 4792 137 44 32 25
2010 4932 137 37 32 23
2011 5010 137 35 32 28
2012 6770 137 52 32 19
2013 8592 137 34 32 18
2014 10566 137 32 2015 12606 137 32
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Figure 13, Wood Pole Management Model Performance compared to Actual for 2013
Outcome
Based on the KPI’s and metrics above, the WPM program accomplishes its objectives. Our reliability continues to improve which helps keep our costs and ultimately customer rates down. This work provided
the evidence needed to help drive other related programs on the Electrical Distribution system.

From all of this program and other related programs, Avista continues to evolve from a very reactive
organization into pro-active organization. As shown in Figure 14, our annual number of events recorded in our Outage Management Tool (OMT) has stabilized around 13,000 events per year after 2009 when the
WPM program was fully implemented. When you remove the planned maintenance related outages out of
the total number of events, Figure 14 shows the number of un-planned events trending downwards. So
Avista continues to generally see the same SAIFI values but now we are fixing the problem before it
becomes a failure. The WPM related outage events in OMT have been cut in half since 2008 as shown in Figure 15. For 2013, we estimate we gained $10 million worth of benefits by avoiding outages when you
compare what we predicted to occur without a program and what we actually saw.

Ultimately, this program has proven itself very successful. It continues to improve our reliability.

Staff_PR_050 Attachment F Page 15 of 17
Figure 14, Annual Number of Recorded Events and the Number of Events with all of the Planned Activities Removed

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Figure 15, Number of Events in OMT related to Assets covered by the WPM Program

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