Search

This article presents a quantified method to evaluate an overall condition index of power equipment and its probability density distribution using monitored data. A special transformation is developed to normalize the monitored data of different parameters. The overall condition index can be easily calculated using the normalized parameters. To capture the uncertainty and randomness of the monitored data, the kernel density evaluation method is used to assess the probability distribution of the condition index. With the probability density function, the probability of power equipment operating in the normal condition can be assessed. The monitored data of two breakers for five monitored parameters are used in the case study. The results indicate that the proposed method can straightforwardly identify which breaker has a worse performance and, therefore, should be considered for maintenance first. [ABSTRACT FROM AUTHOR]