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《Power System and Clean Energy》 2015-11
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Application of the Time Series Based on the Seasonal Decomposition in the Main Transformer's Defect Rate Prediction

LI Xun;ZHANG Hongzhao;YAO Senjing;HUANG Ronghui;LIU Shungui;Lü Qishen;ZHANG Lin;Shenzhen Power Supply Co., Ltd.;  
Aiming at the nonlinear and non-stationary characteristics of the main transformer defect rate series and the seasonal characteristics of the occurrence of the main trans-former defects, this paper proposes that the defect rate of the main transformer be predict by combining the seasonal decomposition of the main transformer defect rate series and time-series ARIMA prediction to explore more effective defect data.First of all, the original series is pre-processed, and it is broken into a series of different model components, which can highlight the local feature information of the original main transformer defect rate series; Secondly, each component is analyzed and according to its change law, the mathematical model is set up using time series method and prediction is made. This method can not only simplify the model built but also reduce the interference and the coupling between the different components. Finally the predicted value of each component is added up to obtain the predicted value of the defect rate. The numerical example results show that the proposed method has good prediction effect.
【Fund】: 南方电网公司重点科技项目(2013科资0038)~~
【CateGory Index】: TM407
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