Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model

Jun Lin,Gehao Sheng,Yingjie Yan,Jiejie Dai,Xiuchen Jiang

doi:10.3390/en11010225

Jun Lin, Gehao Sheng + Show 3 more

Open Access

https://doi.org/10.3390/en11010225

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Journal: Energies	Publication Date: Jan 17, 2018
Citations: 27	License type: CC BY 4.0

Affiliation: Shanghai Jiao Tong University

Abstract

The purpose of analyzing the dissolved gas in transformer oil is to determine the transformer’s operating status and is an important basis for fault diagnosis. Accurate prediction of the concentration of dissolved gas in oil can provide an important reference for the evaluation of the state of the transformer. A combined predicting model is proposed based on kernel principal component analysis (KPCA) and a generalized regression neural network (GRNN) using an improved fruit fly optimization algorithm (FFOA) to select the smooth factor. Firstly, based on the idea of using the dissolved gas ratio of oil to diagnose the transformer fault, gas concentration ratios are also used as characteristic parameters. Secondly, the main parameters are selected from the feature parameters using the KPCA method, and the GRNN is then used to predict the gas concentration in the transformer oil. In the training process of the network, the FFOA is used to select the smooth factor of the neural network. Through a concrete example, it is shown that the method proposed in this paper has better data fitting ability and more accurate prediction ability compared with the support vector machine (SVM) and gray model (GM) methods.

Highlights

Transformers are an important component of a power system
The accuracy of predictions of gas concentration in oil needs to be improved. In view of these two problems, this paper proposes the kernel principal component analysis (KPCA)-fruit fly optimization algorithm (FFOA)-generalized regression neural network (GRNN) model to predict the gas concentration in transformer oil
The results show that the proposed method has better global convergence and improves the accuracy and reliability of the prediction process

Summary

Introduction

Transformers are an important component of a power system. Its running state can directly affect the reliability of the entire power supply and system stability. The authors of [12] proposed a deep belief network (DBN) approach to predict transformer concentrations They achieved good prediction accuracy, but their method ignores the relationship between the individual gas components. The accuracy of predictions of gas concentration in oil needs to be improved In view of these two problems, this paper proposes the KPCA-FFOA-GRNN model to predict the gas concentration in transformer oil. In order to solve the problem of transformer fault diagnosis, the authors of [16] obtained 34 eigenvectors by combining electrical experiment data with dissolved gases in oil and uses the KPCA method to reduce dimensions. In [23], a better prediction result of distributed power generation is obtained using the SVM model with FOA optimization parameters. The experimental results show that the method proposed in this paper has high prediction accuracy

Related Theory

The KPCA-FFOA-GRNN Prediction Model

Prediction of Dissolved Gases Concentration

Prediction Method

Application in Pre-Emergency State of Transformer

Findings

Conclusions