Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers

Tusongjiang Kari, Wensheng Gao, Ayiguzhali Tuluhong, Yilihamu Yaermaimaiti and Ziwei Zhang
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Tusongjiang Kari: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Wensheng Gao: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Ayiguzhali Tuluhong: School of Electrical Engineering, Xinjiang University, Urumqi 830046, China
Yilihamu Yaermaimaiti: School of Electrical Engineering, Xinjiang University, Urumqi 830046, China
Ziwei Zhang: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

Energies, 2018, vol. 11, issue 9, 1-19

Abstract: Forecasting dissolved gas content in power transformers plays a significant role in detecting incipient faults and maintaining the safety of the power system. Though various forecasting models have been developed, there is still room to further improve prediction performance. In this paper, a new forecasting model is proposed by combining mixed kernel function-based support vector regression (MKF-SVR) and genetic algorithm (GA). First, forecasting performance of SVR models constructed with a single kernel are compared, and then Gaussian kernel and polynomial kernel are retained due to better learning and prediction ability. Next, a mixed kernel, which integrates a Gaussian kernel with a polynomial kernel, is used to establish a SVR-based forecasting model. Genetic algorithm (GA) and leave-one-out cross validation are employed to determine the free parameters of MKF-SVR, while mean absolute percentage error (MAPE) and squared correlation coefficient ( r 2 ) are applied to assess the quality of the parameters. The proposed model is implemented on a practical dissolved gas dataset and promising results are obtained. Finally, the forecasting performance of the proposed model is compared with three other approaches, including RBFNN, GRNN and GM. The experimental and comparison results demonstrate that the proposed model outperforms other popular models in terms of forecasting accuracy and fitting capability.

Keywords: dissolved gas content forecasting; mixed kernel function; genetic algorithm; support vector regression; power transformer (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 references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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