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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.

Frequency response analysis (FRA) demonstrates significant advantages in the diagnosis of transformer winding faults. The instrument market desires intelligent diagnostic functions to ensure that the FRA technique is more practically useful. In this paper, a hierarchical dimension reduction (HDR) classifier is proposed to identify types of typical incipient winding faults. The classifier procedure is hierarchical. First, measured frequency response (FR) curves are preprocessed using binarization and binary erosion to normalize FR data. Second, the pre-processed data are divided into groups according to the definition of dynamic frequency sub-bands. Then, hybrid algorithms comprised of two conventional and two novel quantitative indices are used to reduce the dimension of the FR data and extract the features for identifying typical types of transformer winding faults. The classifier provides an integration of a priori expertise and quantitative analysis in the furtherance of the automatic identification of FR data. Twenty-six sets of FR data from different types of power transformers with multiple types of winding faults were collected from an experimental simulation, literature, and real tests performed by a grid company. Finally, real case studies were conducted to verify the performance of the HDR classifier in the automatic identification of transformer winding faults.