Contrary to rumor, the grid won’t die, but in fact must grow exponentially, in function, complexity, and usefulness.
Analyzing Asset Failures
Simulation modeling can improve O&M and capital-planning processes.
Electric utilities are faced with the challenge of managing a range of aging distribution assets that are critical to system reliability. They also are threatened with potentially huge costs as they seek to replace these assets over the coming years to maintain reliability. Making intelligent decisions about asset maintenance and replacement requires accurate information about the failure patterns of these assets over time. However, most data elements that could shed light on such patterns—asset condition, joint use, maintenance patterns, or results of stratified inspection—are not widely available. Still, utilities must forecast capital and O&M spending requirements each year, regardless of their understanding of such asset failures.
In addition to these gaps in data, a further lack of tools and processes make it difficult to support budget-allocation decisions. For the most part, capital funding decisions are being made using the simple but potentially inaccurate forecasting method of taking the average of asset failures over a certain period of time. Existing replacement or maintenance strategies don’t necessarily match the costs and reliability that will be experienced in several years.
To truly understand how an asset class fails over time, it’s essential that utility companies capture and store historical data about asset failures. A database with critical asset attribute elements can provide insight in to the pattern of failures and establish the framework for a probabilistic model that can replicate the failure patterns over time in a simulated environment. Such an approach can help utilities in their efforts to develop effective O&M and capital replacement strategies.
Engineers often try to generate survivor curves for assets. If a utility can track an asset group from the time the assets were placed into service until the of that asset-year group was removed from service, then analyses such as survivor curves can be applied to assets of that group.
However, survivor curves generally yield unrealistic models, because of difficulties in defining and capturing the data that would enable a cradle-to-grave analysis of asset-failure patterns. Further, by the time a survivor curve has been generated for an asset, newer technologies are usually replacing that asset.
Nevertheless, basic asset-life characteristics, such as when an asset was put in service and when it failed generally are available. At a strategic level, this basic data allows a robust analysis of failure patterns and provides insight for predicting how much capital will be required to replace failed assets. The key requirement is assessing the probabilistic nature of the failure; then one must understand the differences in mitigation options, and the associated reliability and financial impacts. This allows asset managers to clearly communicate and support their asset-replacement requirements to senior management and to regulators.
Condition-based asset modeling provides a way to scrutinize asset failure patterns and can enhance a probabilistic model. However, the benefits of this type of modeling can’t