Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations

Fabio Pereira,Shigueru Junior,Josemir Santos,Francisco Bezerra,Gilberto Souza,Ivan Chabu,Fábio Micerino,Silvio Nabeta

doi:10.3390/en11071691

Abstract

Transformers are one of the most important part in a power system and, especially in key-facilities, they should be closely and continuously monitored. In this context, methods based on the dissolved gas ratios allow to associate values of gas concentrations with the occurrence of some faults, such as partial discharges and thermal faults. So, an accurate prediction of oil-dissolved gas concentrations is a valuable tool to monitor the transformer condition and to develop a fault diagnosis system. This study proposes a nonlinear autoregressive neural network model coupled with the discrete wavelet transform for predicting transformer oil-dissolved gas concentrations. The data fitting and accurate prediction ability of the proposed model is evaluated in a real world example, showing better results in relation to current prediction models and common time series techniques.

Highlights

As the transformer is one of the most important unit of an electrical system, it is natural that efforts are made to preserve its integrity and increase its availability [1]
We evaluated the effect of the time delay parameter d on the performance of the training process, evaluated using the mean squared error and the coefficient of determination R, which is a goodness-of-fit measure for linear regression between the target and the predictions
This study proposes a combination of a nonlinear autoregressive neural network model with the discrete wavelet transform for predicting power transformer oil-dissolved gas concentrations

Summary

Introduction

As the transformer is one of the most important unit of an electrical system, it is natural that efforts are made to preserve its integrity and increase its availability [1]. For these purposes, maintenance policies and procedures are planned and applied to ensure the least interruption of such equipment [1,2,3]. Regarding oil-filled transformers, the maintenance operations should be carried out with additional caution to minimize the potential problem of flammability of the thermal insulation material [4]. Several other tests of insulation items have been an important part of transformers fault diagnosis systems, with emphasis on chromatographic oil-dissolved gas analysis, namely dissolved gas analysis (DGA) [4,5,6,7,8,9]

Results

Discussion

Conclusion