改进灰色GM(1,m)模型在变压器故障预测中的应用

针对灰色模型在预测变压器故障时对波动数据序列的预测误差较大的问题,提出了一种灰色GM(1,m)预测模型改进方案:对原始数据序列进行处理,使其具有更好的指数规律,以满足预测模型对光滑性的要求;对处理过的原始数据序列进行灰关联度分析,以得到各变量之间的关系;优化预测模型的背景值并用其建模;采用等维新息模型预测数据。采用改进的灰色GM(1,m)模型预测某变压器油中7种特征气体的体积分数,所得预测数据的平均残差和后验相对误差均小于GM(1,1)模型和传统GM(1,m)的预测结果,表明其具有更好的预测精确度。

Application of Improved Grey GM(1,m) Model in Fault Prediction of Transformer

In order to solve problem of big error in predicting data sequence with fluctuation while using grey model to predict transformer fault,an improvement scheme of gray GM(1,m) predicting model was proposed.In the scheme,original data sequence is processed for better exponential rule to meet smoothness requirement of the predicting model,and the processed data sequence is analyzed by grey relational degree method to get relationship between variables.Background value of the predicting model is optimized and used for establishing the model,and equal-dimension new-information model is used to predict data.The improved GM(1,m) model was used to predict volume fraction of seven kinds of characteristic gases in some transformer oil and both average differences and posteriori relative error of predicted data were smaller than the ones of GM(1,1) model and traditional GM(1,m) model,which showed the improved grey GM(1,m) has better prediction accuracy.