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 (r2) 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.
Highlights
Power transformers is some of the most vital and expensive devices in power grids
Many approaches based on artificial intelligence (AI) have been proposed and applied for forecasting the concentration of dissolved gases in power transformers, such as grey model (GM) [10], artificial neural networks (ANN) [11,12,13,14,15], least squares support vector machine (LSSVM) [16,17,18,19] and support vector regression (SVR) [20,21], etc
After the historical data are divided into a training set and a testing set, a forecasting model based on MFK-SVR is trained to predict the development trend of the dissolved gas content in a power transformer
Summary
Power transformers is some of the most vital and expensive devices in power grids. They play a significant role in transferring energy and converting voltages to different levels. Many approaches based on artificial intelligence (AI) have been proposed and applied for forecasting the concentration of dissolved gases in power transformers, such as grey model (GM) [10], artificial neural networks (ANN) [11,12,13,14,15], least squares support vector machine (LSSVM) [16,17,18,19] and support vector regression (SVR) [20,21], etc. The grey model method is capable of providing desirable forecasting result with small-scale data and has been used to predict dissolved gas concentrations in power transformers. Forecasting of dissolved gas content of power transformers is a non-linear time series problem.
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