FERC Orders 890 and 1000 have opened the doors to independent transcos, heralding an era of innovation to solve reliability and capacity problems.
Analyzing Asset Failures
Simulation modeling can improve O&M and capital-planning processes.
be realized without historical failure data. Condition-assessment programs largely have been viewed as O&M expenses subject to elimination or reduction to meet cost-cutting mandates. As a result, asset-condition data is unreliable, leaving utilities to make estimates and assumptions about replacement parameters, condition-based hazard functions and asset conditions.
Asset Failure Curves
Given the lack of cradle-to-grave data and information on asset conditions, a suitable alternative is to gather asset inventory information that includes, at a minimum, installation dates and, for failed assets, the corresponding failure dates. Indeed, many of the available asset data sets are structured in this way.
Establishing a clear definition of failure for an asset class is the initial step in developing an understanding of when assets fail. Analyzing the data in terms of in-service and failure dates allows a utility to see how asset failures are distributed by age, and at what age most failures are occurring. A unique failure frequency probability curve can be created for each asset class (see Figure 1) .
Where possible, the asset-failure data are stratified according to multiple asset characteristics to create a separate curve for each subset of the failure. As an example, transformers as a class have a unique pattern of failure, but one failure frequency curve generalizes the analysis. If the manufacturer or load of the transformer can be shown to have a correlation to failures that is statistically valid in stratifying the data set, then multiple failure-frequency curves can be created from an initial single failure frequency curve for transformers. This in turn allows for a more detailed and robust analysis of failures and forecasting.
Such a failure analysis yields strong results if the historical data establishes trends of failures based on the data itself and not assumed parameters. These failure probabilities are captured over time on an annual basis, providing updated data and allowing for a probabilistic model that is extremely easy to use and produces results that are easily communicated to decision makers.
A potential weakness in failure-frequency analysis is that newer asset technologies have not aged enough to provide thorough insight into the pattern of failure associated with the failure frequencies. A Weibull probability distribution analysis can help address this weakness.
While not easy to explain to those who don’t understand probability distributions or statistics, the Weibull distribution has been used by engineers for many years to model the randomness of asset failures and understand the probabilities, risk and mitigation possibilities (See Figure 2) . A traditional two-parameter Weibull distribution can be configured to capture infant mortality of equipment, attrition (random events that can put an asset out of service, such as vehicles hitting a pole or cable dig-in), or failure due to aging. However, the Weibull distribution typically only captures one of these failure modes at a time. A unique attribute of the Weibull distribution is that the sum of Weibull distributions can be modeled as one distribution by adjusting the parameters and ensuring the relative importance of each element is captured as well. A four-paremeter Weibull distribution allows the entire life cycle