Abstract
Transformer state forecasting and fault forecasting are important for the stable operation of power equipment and the normal operation of power systems. Forecasting of the dissolved gas content in oil is widely conducted for transformer faults, but its accuracy is affected by data scale and data characteristics. Based on phase space reconstruction (PSR) and weighted least squares support vector machine (WLSSVM), a forecasting model of time series of dissolved gas content in transformer oil is proposed in this paper. The phase spaces of time series of the dissolved gas content sequence are reconstructed by chaos theory, and the delay time and dimension are obtained by the C-C method. The WLSSVM model is used to forecast time series of dissolved gas content, the chemical reaction optimization (CRO) algorithm is used to optimize training parameters, the bootstrap method is used to build forecasting intervals. Finally, the accuracy and generalization ability of the forecasting model are verified by the analysis of actual case and the comparison of different models.
Highlights
A power transformer is among the key equipment of a power system
The kernel function of radial basis function (RBF) is used in this paper, a and b can be calculated by Equation (17), so the nonlinear prediction model of least squares SVM (LSSVM) can be obtained as Equation (18): N
Based on the correlation between dissolved gas contents and variation and transformer faults, fault diagnosis methods or models recommended by International Electrotechnical Commission (IEC) and Institute of Electrical and Electronics Engineers (IEEE) have been produced, which are divided into two categories
Summary
A power transformer is among the key equipment of a power system. During the long operation of the transformer, due to equipment aging, discharge fault, thermal fault, and other reasons, a small amount of gas will be produced in the insulation oil, and the content of various components of dissolved gas and the proportion of components in the oil are closely related to the operation condition of the transformer. The existing methods of dissolved gas content forecasting in transformer oil can be summarized into three categories: methods based on statistics, intelligent models, and combined models. Intelligent forecasting methods are typically represented by artificial neural network (ANN) [8], recurrent neural network (RNN) with network structure of loop feedback [9], and long short-term memory (LSTM) [10] These methods analyze and train a large amount of historical data to obtain forecasting models that can reflect the development trend of time series. A phase space reconstruction (PSR) chemical reaction optimization (CRO) weighted least squares SVM (WLSSVM) model combining chaos theory and weighted least squares support vector machine is proposed to forecast the time series of dissolved gases in power transformer oil. Conclusions are drawn, and potential future work is discussed is Section 6
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