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.
of an asset to be modeled, including infant mortality, attrition, and age.
The Weibull distribution can be used to smooth failure-frequency curves and interpolate the probability of failure for an asset class in later years—even when that later data is missing, as it is for newer technology. An example of this is XLP cable. As one of the newer underground cable technologies, there are no assets that have reached 80 years of in-service time. Forecasting the failures of these assets based on historical failures ignores the fact that some of these cable elements could continue to survive long after the failure-frequency curve analysis indicates. The four-parameter distribution takes this into account.
Utilities have a choice in modeling aging asset-failure patterns. They can use simple failure frequency curves, derived purely from available data showing a ratio of failures to total inventory. An advantage of this method is that it’s relatively easy to explain. Alternately, they can use a fitted Weibull curve, which adds a theoretical dimension and is more complicated than a simple failure-frequency curve. This approach can yield greater precision, but the results easily can be misunderstood in discussions with decision makers.
Creating a Simulation Model
Understanding asset failure patterns can provide insight by itself, but generally doesn’t provide a vehicle for analyzing scenarios to mitigate failures. Taking failure analysis and Weibull distribution curves a step further, operators might consider the financial and reliability implications of run-to-fail strategies and proactive replacement approaches. These considerations require the capability to evaluate differing strategies, and, just as important, to compare the strategies to find the best solution. A rigorous asset-management and life-cycle analysis methodology depends on the ability to accurately evaluate competing asset-management strategies.
This type of temporal analysis can be accomplished with a probabilistic discrete-event simulation model. Such a model incorporates a time clock, so at the start of a simulation run, the model understands the characteristics of an asset and can age it over a given time horizon. By incorporating failure-frequency probabilities or set Weibull probability distributions, a simulation model can track failures, mitigate these failures in the model environment with evaluated strategies, and can predict future spending and reliability impacts.
A simulation model differs considerably from standard forecast techniques. It plays out the aging process and parallel events, such as replacement and maintenance, accordingly moving assets back and forth along the age spectrum. That allows users to predict failures far into the future, and also the age profile of the resulting inventory at different points in time, as well as financial and reliability implications associated with various failure-mitigation strategies.
For example, a run-to-failure strategy can defer some capital costs, but can impair system-reliability statistics, such as SAIDI and SAIFI. A proactive replacement strategy might show sharp near-term financial spikes in capital spending, but the programmed replacements would improve reliability as the assets are replaced prior to failure.
A simulation model can allow operators to evaluate a mixture of different strategies, in effect allowing them to experiment with asset-management programs. For example, a manager might consider reactively replacing failed assets with a new