  In recent years, under deregulated environment, electric utility companies
have been encouraged to ensure maximum system reliability through the
employment of cost-effective long-term asset management strategies. To help
achieve this goal, this research proposes a novel statistical approach to
forecast power system asset population reliability. It uniquely combines a few
modified Weibull distribution models to build a robust joint forecast model. At
first, the classic age based Weibull distribution model is reviewed. In
comparison, this paper proposes a few modified Weibull distribution models to
incorporate special considerations for power system applications. Furthermore,
this paper proposes a novel method to effectively measure the forecast accuracy
and evaluate different Weibull distribution models. As a result, for a specific
asset population, the suitable model(s) can be selected. More importantly, if
more than one suitable model exists, these models can be mathematically
combined as a joint forecast model to forecast future asset reliability.
Finally, the proposed methods were applied to a Canadian utility company for
the reliability forecast of electromechanical relays and the results are
discussed in detail to demonstrate the practicality and usefulness of this
research.
