Advanced distribution transformer asset management embracing its relocation: A cost saving approach

Abstract

Change in customers' consumption patterns due to the introduction of new electricity consuming technologies into human consumption practices has impacted the existing optimal loading profile of the grid facilities. Although some MV/LV distribution transformers (DTs) are operating under a light load profile, this does not guarantee that they are in a healthy condition, since necessary maintenance procedures are required. On the other hand, many unplanned overloading conditions occur on the other DTs and causes accelerated failures which contributes to very high operational costs. This paper presents the outcome of a study on how the planned DTs relocation can save asset managers against their huge DTs replacement costs developed due to the highly overloaded DTs failures in a utility, and how it will improve the overall reliability of the DTs. A mixed-integer linear programming (MILP) solution is proposed to formulate the introduced economic relocation-based asset management (AM) framework while minimizing total cost (TC) of the network. The results show that incorporating relocation techniques into the AM formulation ends in reduction of the distribution network TC and a decrease in asset failure rates if compared with application of traditional techniques.

Condition Assessment of Medium Voltage Assets: A Review

“…Aged DT is a problem for electric utilities because the assets are more prone to failures and large-scale replacement of aged units is prohibitive due to high costs. Furthermore, the widespread incorporation of modern electrical loads and the unforeseen growth of energy demand result in unplanned overloading from the usual operation conditions [82], increasing probability of failures and operational costs [83].…”

“…For instance, FRA is a non-invasive method in the frequency domain that detects mechanical and electrical damage such as winding deformations and displacements inside the DT. FRA is an effective test for DT monitoring, however, it can be affected by network conditions during online application [83]. Therefore, interpretation of FRA results still requires further research because of its complexity, including new computational methods such as AI-based diagnostic techniques, e.g., [87].…”