State Prediction of Power Transformer Based on Grey-Lagrange Method with Weighted Coefficient of Variation

Transformer is the key equipment of the power system. The reliability of power system is reduced by transformer faults. The device, which monitors and predicts the operational status of the transformer, has important implication for the safe operation of the power system. Currently, most of the relevant work is based on the dissolved gas analysis (DGA), but the raw data usually has short-period fluctuations, which poses great difficulties for the accuracy of prediction model. Transformer fault is a comprehensive performance of the historical characteristics and abrupt characteristics. Model building of existing methods usually leads to lasting dependence on data quality. In this paper, state prediction of power transformer based on Grey-Lagrange method with weighted coefficient of variation was proposed. Firstly, the characteristics of the faults development are taken into account comprehensively. The long-term and short-term data sets are prepared to build the prediction model, which is conducive to improving the accuracy of the model. Secondly, the coefficient of variation matrices was calculated and used as the input characteristics to train the Grey-Lagrange model. Finally, the models which have been trained were merged by genetic algorithm (GA). In summary, the method, proposed in this paper, can recognize the change in detail and predict the trend of gas content in oil, which can provide assistance for condition maintenance. The results of the prediction model offer the possibility to estimate the states of the equipment, especially for the situation of weak insulation degradation and abrupt faults.