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

Jun Lin, Gehao Sheng, Yingjie Yan, Jiejie Dai and Xiuchen Jiang
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Jun Lin: Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China
Gehao Sheng: Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China
Yingjie Yan: Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China
Jiejie Dai: Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China
Xiuchen Jiang: Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China

Energies, 2018, vol. 11, issue 1, 1-13

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.

Keywords: dissolved gas in oil; kernel principal component analysis; fruit fly optimization algorithm; generalized regression neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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