Probabilistic Methods for Assessing Spare Distribution Power Transformers

Abstract

This paper addresses the challenge of determining the optimal number of spare transformers needed to maintain reliability in power distribution networks. Given the critical role of transformers in ensuring uninterrupted power supply, their failure can significantly impact the distribution system's operation. To mitigate these risks, the study explores probabilistic methods, specifically the Poisson Distribution and Markov Models, to optimize the inventory of spare transformers. The reliability criteria guide the analysis, ensuring that the system remains reliable while minimizing the financial burden of maintaining excess inventory. A case study demonstrated the comparison between two probabilistic methods using reliability criteria. The optimal number of spare transformers generated through calculations using both methods shows relatively similar results. The results highlight that while both methods are effective, the Markov Model offers a more comprehensive approach by incorporating additional parameters that accurately reflect actual conditions. This model enables utilities to balance system reliability with cost efficiency, ensuring that spare transformer inventories are maintained optimally without unnecessary expenditure. The calculation results will be compared with historical transformer failure data in the system, which are used as part of the study to validate the calculated data and demonstrate the effectiveness of both methods.