Is Reliability-Centered UVM a Standard of Care?

Is Reliability-Centered UVM a Standard of Care?

Article
by Will Porter, Director of Research, Development and Industry Intelligence

CN Utility Consulting (CNUC) Director of Research, Development and Industry Intelligence Will Porter wrote an article titled “Data Driven Methodologies: Is Reliability-Centered UVM a Standard of Care?” that will be published in the upcoming issue of Transmission and Distribution World Magazine.

This article focuses on three findings that evaluate how utility vegetation management has conformed to reliability data and how it affects the industry. The following discussion demonstrates the theory, the logic and the empirical evidence that we should be supporting UVM beyond the current reliability-centered maintenance. Read the full article below to learn more.

DATA DRIVEN METHODOLOGIES: IS RELIABILITY CENTERED UVM A STANDARD OF CARE?

A decade agom utility vegetation management (UVM) subject matter experts were encouraging utility executives to realign their UVM programs for measurable reliability benefits. It was suggested this realignment would result in lower costs and it would bring UVM out of the industrial age and into the technological realities of modern business management. The most significant and clearest measure of reliability improvement has been with extra high voltage transmission because an internationally mandated standard, FAC-003 has compelled specific results and achieved them. The standard affected transmission lines, approximately 3% of the miles of overhead power lines in the U.S. What can we say about the other 97 percent of overhead miles of sub transmission and distribution electric lines in the U.S.? Although the bright light of business models including economies of scale and process management has been shined into the voids of UVM performance, there is no consensus that we have given birth to the golden age of utility arboriculture. Young arborists, some freshly trained about UVM, are still put to daily task to work around lethal conductors ensconced in vegetation. Utility arborists, scolded by the public for being butchers, continue to wonder why we do things the way we do them. Every year a few go to work and never return. Many more tree workers lose their lives or are injured because they made contact with electrical energy, often not knowing the wires were there. It is worth reviewing what we do and the assumptions behind what we do if only to reduce risks to human life.

The following are findings from evaluating how utility vegetation management has conformed to reliability data:

  • Reliability improvement is correlated with increases in customer density
  • When UVM programs are prioritized to improve reliability metrics, long-term UVM workloads are likely to increase
  • Reliability-centered maintenance (RCM) applied to UVM may shift focus away from other objectives
  • An increase in tree-related outages can occur at the same time reliability is improving

The need to apply computer-enabled technology to UVM has coincided with the adoption of IEEE-1366. The Interruption Cost Estimate (ICE) calculator designed by the Department of Energy (DOE) reinforces the nexus between data-driven UVM and a burgeoning growth of reliability data. It would be imprudent to discourage actions that prevent outages that affect large electric loads. However, it is also important to recognize that the priorities of electric system reliability are not the only motivation for performing UVM. The consequences of applying electric system reliability metrics to UVM should be fully vetted. Is it okay that RCM encourages UVM policies that increase the frequency that outages occur?  Improvements for relative reliability and improvements for absolute reliability are two separate outcomes that both require adequate planning and resources to achieve. The following discussion demonstrates the theory, the logic and the empirical evidence that we should be supporting UVM beyond the current RCM.

THEORY
In theory, UVM programs operate upon a set of objectives that are established to support a mission and vision for a utility company. These objectives are collectively the driver and benchmark upon which the program is measured and implemented in a continuous spiral of improvements. Objectives provide the theoretical framework behind UVM. Without such a framework UVM would be reactions to system failures and liability. The cost alone of reactive UVM has driven UVM programs to become more preventative.  To be preventative one must have a plan based on a theory of objectives, strategies and results.

LOGIC
Once the theory behind UVM is established then logical steps are taken to achieve the framework of objectives. Theory may be the the lodestar but logic is the navigation system and it has to adjust to stay on course. For example, a UVM program must show a return on investment, but what logic gets you there? UVM has sought cost-effective ways to manage high risk, but in order to maximize ROI, it has bypassed managing smaller risks and made compromises and adjustments. This strategy has allowed UVM departments to show success by using IEEE reliability metrics. Managers can report success if SAIDI and SAIFI are reduced. For upper management this provides a reduced cost burden achieved through reliability improvements!

Unfortunately, the marriage of UVM risk and reliability metrics has also meant performance for other objectives and risks could be reduced, ignored or transferred away from UVM departments. Worker safety liability has been shifted to contractors. Reliability performance shows UVM is performing what is reasonable and shields utilities from public liabilities. State regulatory compliance validates this idea by requiring reliability metrics. Other objectives like fire prevention, environmental quality, and customer service have become weak objectives. Regardless of relative ranking, each objective should have key performance indicators. Strategies and tactics should be formulated to support measurable achievements and improvements. Some objectives, such as reliability and safety, may be more important, but a program ought to be balanced.  A single objective, such as reliability, should not be the only characteristic of quality service. For example, UVM from a customer perspective is an inconvenient intrusion onto private property where vegetation is negatively altered in order to complete an electric system correction. Asset corrections protect equipment and ensure reliability, which is misconstrued as the primary if not only element of customer service. Why shouldn’t UVM be a customer service activity in which the customer perceives they are receiving a benefit to their property or vegetation and their community? Perhaps we have come to accept adversarial relationships with rate paying customers.

EMPIRICAL EVIDENCE
While logic is necessary to navigate, we need empirical evidence, an analysis of readings from our instrument cluster, to support and measure the performance of our logic. The following four sets of data are readings from the current era of UVM and they suggest that there is a need to change our theoretic lodestar or reevaluate the logic used to navigate our UVM programs.

  1. Reliability is a UVM problem but Reliability-Centered Maintenance is an asset management strategy

Reliability-centered maintenance (RCM) from an asset perspective is designed to address the vast majority of failure modes, which are likely to occur during normal operating conditions. RCM is integral to the lifecycle of assets, which includes constructing, maintaining, extending and finally replacing. Since abnormal conditions are usually an “event” such as a weather, IEEE devised a statistical method to exclude certain outages in reliability performance metrics. The IEEE- 1366 2012 defines a Major Event as one that exceeds reasonable design and or operational limits of the electric power system. A Major Event includes at least one Major Event Day (MED).” A T-MED is a calculated threshold for excluding outages in reliability metrics.  It provides a limit beyond which a utility should not be expected to perform within expected reliability parameters.

Figure 1

Berkeley National Laboratory (BNL) has performed cumulative statistical research on utility reliability data collected over the past thirteen years. Some specific variables are associated with measureable improvement in SAIDI and SAIFI. T&D expenditures, percent of underground, hot weather, and increases in customer density were found to correlate with lower SAIDI and SAIFI. Other variables such as wind are associated with increases in SAIDI and SAIFI (Larsen, 2015). Although the BNL research does not differentiate wind caused outages between tree and equipment failures, CN Utility Consulting (CNUC) benchmark surveys have found at least 43% of outages during major event days (MED) are tree related (CNUC, 2015). Wind-event data were studied at BNL including and excluding MED. If MED are included, a 10% increase in average annual wind speed is correlated with a 75% increase in SAIDI. In contrast, if MED are excluded, a 10% increase in average annual wind speed is correlated with only a 2% increase in SAIDI (Larsen, 2015) (See Figure 1). This data analysis is significant to UVM since the majority of tree-related outages are caused by wind and other loading events. Without actionable knowledge of the life cycle of trees and their failure modes and without a reliability system designed around the effective management of ROW land and nearby trees, RCM is not likely to address tree-related reliability nearly as well as it can guide asset management.

  1. Reliability Performance is not measured the same from one company to the next

The CNUC benchmark surveys found that 88% of responding companies track tree-related SAIFI, SAIDI and CAIDI and 74% of companies are using this information to make planning and resourcing decisions for the UVM department (CNUC, 2014). Additionally, 59% of companies have not strictly used the IEEE-1366 guidelines for separating major event days (MED) from non-MED (CNUC, 2014). In fact, the 1366-2012 revisions left the door open for companies to adopt their own specific definition of a catastrophic storm. Some companies may have to comply with a state commission definition. Differences in MED definitions and thresholds may effectively increase or decrease the number of events which become MEDs and subsequently influence the threshold for determining MED. When one major event is excluded, the average used to determine the T-MED is also lowered, which causes additional events to be classified as major. Consequently, the measurement of reliability performance is inconsistent and possibly misleading because SAIFI and SAIDI numbers vary significantly depending on whether outage events are included or excluded.

  1. Reliability is worsening according to SAIDI

Despite the reported improvements to non-MED SAIDI, the most current research into reliability data indicates that distribution system SAIDI measurements are worsening by 10% annually over the thirteen-year period of study (Larsen, 2015). In recent years major storm events have become a critical reliability issue and tree related outages are the chief contributor.  A comparison of the ratios of tree-related to system SAIFI reveals Non-MED tree related SAIFI was on average 24% of system non-MED SAIFI. In contrast, MED tree-related SAIFI was 43% of the system MED SAIFI (CNUC, 2015). This difference underscores the significance of the SAIFI amplitude that vegetation causes during MEDs compared to other distribution system failure modes. The risk factor for major event days is greater for UVM than it is for equipment failure in the absence of tree damage. The Larsen study, which has many interesting findings, also found a 10% decrease in precipitation is correlated with a 3% increase in SAIFI (Larsen, 2015). It has been thought that drought conditions may contribute to tree-related outages.

  1. A Case Study

The following is a comparison study between SAIFI, SAIDI, and tree-related outages per pole mile between three companies (X, Y and Z). The UVM programs for these utilities represent a reliability-centered maintenance program (Company X), a compliance-based program (Company Y) and a company (Company Z) which had favorable reliability performance but was dissatisfied with its UVM program.  All three companies were excellent performers based on SAIDI and SAIFI. Company Z initiated the study with CNUC by asking the question, “How is it possible that we achieve best-in-class reliability when we know the UVM program is not meeting best management practices?” By comparing the metrics from the three companies within a field of other companies, CNUC found several revealing facts that demonstrate the limitations for using reliability metrics as a measure of the standard of care for UVM.

Figure 2

Company X almost doubled the number of tree-related outages on its distribution system in five years (2008-2012) and still showed significant improvements in SAIFI (31%) for the same years (Figure2). By focusing UVM efforts on high customer density feeder lines while deferring maintenance on single phase lines with lower customer density, X was able to report almost best-in-class metrics in 2012.  An increase in SAIFI in 2011 could have been an anomalous year given the return to dropping SAIFI values in 2012. 2013 and 2014 suggest something else is happening.  Potentially there is an upper limit of tree-related outages at which point SAIFI will also increase (2012-2014 in Figure 2).

Another interesting aspect of the reliability metrics occurs when SAIFI increases faster than SAIDI, then CAIDI will actually improve, in spite of an overall worsening of the other metrics. This is another example of how reliability metrics may be misleading. A company could show exemplary reliability in comparison to industry averages by only focusing on non-major events, at the same time that vegetation-related SAIDI, SAIFI and outages are increasing. Outstanding reliability is a product of interpreting statistics and focusing on the bigger picture of utility reliability, which include other failure modes such as equipment failure and generic categories such as weather events that are non-major. If there are improvements to the asset, such as replacing equipment, adding isolating fuses, monitoring with sensors and making equipment more resistant to weather, vegetation, and animals, then reliability will improve and it can appear as though the vegetation is under better control. In reality it is the asset that is improved, not the vegetation management.

Figure 3

Figure 4

A comparison of reliability indices for the three companies of interest (X, Y, and Z) show that all three utilities show excellent metrics when compared with the UVM industry in general (Figure 3 and 4). If the graphs only included peer utilities (companies with similar key characteristics), all three companies are best-in-class among their comparators. These two figures also show that X and Z, RCM companies, compare well with Y, the compliance-based program. Of note, a majority of the other companies that are in the first quartile have cycle-mandated programs (regulatory-based programs).

As noted above in Figure 2, company X is able to achieve a high reliability performance as measured by SAIFI while the absolute number of outages has increased 143% over a nine-year period. Company Z knew that there was something misleading about their best-in-class tree-related reliability metrics, because the increases in the percent of trees in contact with powerlines and the increases in the percent of reactive maintenance were not represented by these measurements. What wasn’t clear to company Z was the relative value of SAIFI and SAIDI. In other words, IEEE metrics are customer-density dependent and UVM is tree-density dependent. When comparing companies X, Y and Z with peers using an absolute rate (outages per mile), a different picture emerges (Figure 5). Company Y, which is focused on regulatory mandates, achieves similar reliability metrics as the RCM company X, while meeting other objectives as well. These objectives include public and worker safety, fire risk reductions, and environmental quality. As figure 5 shows, this results in best-in-class reliability measured as an absolute rate.

Figure 5

CONCLUSION
Reliability is a major objective, especially as we enter into an era when climate changes are a significant threat to utility operations. However, effective vegetation management is not a direct response to conditions that negatively impact reliability. The reliability metrics that currently guide vegetation management do not measure or recognize the full extent of the UVM workload or forestry conditions. It is not the incremental tree growth that impacts reliability but rather the accumulative growth which leads to branch and tree failure. Nonetheless, it is still growth that UVM must control in order to prevent interruptions. This growth component must be managed in a cost-effective way and UVM must be performed to satisfy other objectives besides reliability. The use of reliability metrics as a measurement of UVM efficacy is pressuring utilities to practice reliability-measured UVM rather than sustainable UVM that adheres to principles of forestry and urban arboriculture. All vegetation near power-lines must be managed at some point, regardless of its impact on reliability. Delaying maintenance to serve improvements to reliability metrics compromises all of the objectives and creates a greater long-term risk. This research was performed to demonstrate the shortcomings of reliability-centered maintenance and offer some alternatives to steer UVM in the direction of a more balanced approach that includes multiple objectives.

 

WORKS CITED:

CNUC. (2014). 2011-2012 Distribution CN Utility Distribution Benchmark Survey Analysis Updated 2014. Sebastopol, California.

CNUC. (2015). Distribution Benchmark Survey Results 2014. Sebastopol, CA: CN Utility Consulting.

Larsen, P. H. (2015, August). Assessing Changes in the Reliability of the U.S. Electric Power System. Retrieved from Berkley Lab Electricity Markets and Policy Group: http://emp.lbl.gov/publications/assessing-changes-reliabi

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