Combining Modified Weibull Distribution Models for Power System Reliability Forecast

TL;DR

This paper introduces a novel statistical approach that combines modified Weibull distribution models to improve power system asset reliability forecasting, aiding utility companies in cost-effective asset management.

Contribution

It proposes a new method to select and combine Weibull models for more accurate reliability forecasts of power system assets.

Findings

Effective model selection for asset reliability prediction

Successful application to Canadian utility data

Enhanced forecast accuracy through model combination

Abstract

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.