Charge management of plug-in electric vehicles for distribution transformer life enhancement

J. R. da Silva *, P. M. de Oliveira, T. R. Lima, A. Z. de Freitas

Electronic and Electrical Engineering Department, Nove de Julho University, São Paulo, SP, Brazil

Abstract

A large population of power transformers along with other power system grid infrastructures have been in service for decades and considered to be in their final ageing stage. On the other hand, due to economy and business growth in our era, the electricity demand is growing rapidly. Therefore, transformers became the most critical devices in power system due to their long repair or replacement time. Plug-in electric vehicles (PEV) have been identified as an option that can reduce criteria pollutant and greenhouse gas emissions associated with the transportation sector. The electricity demand of one of these vehicles is comparable to that of a typical U.S. household and thus clustering of PEVs in a neighborhood might have adverse effects on the transformer and disruption of service. In this paper, the electricity demand of a neighborhood is modeled based on measured vehicle and household data. Then the threshold temperature is determined to program the charging process. In the proposed method, we used adaptive neuro-fuzzy inference system (ANFIS). Simulation results show that the ANFIS can accurately predict the spot hot of transformer.

Keywords

Transformer, Hot spot, ANFIS, PEV

Digital Object Identifier (DOI)

https://doi.org/10.21833/AEEE.2019.05.003

Article history

Received 25 December 2018, Received in revised form 18 March 2019, Accepted 22 March 2019

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How to cite

da Silva JR, de Oliveira PM, and Lima TR et al. (2019). Charge management of plug-in electric vehicles for distribution transformer life enhancement. Annals of Electrical and Electronic Engineering, 2(5): 14-18

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