基于IDBO-ARIMA的电力变压器振动信号预测

Power transformer vibration signal prediction based on IDBO-ARIMA

为解决电力变压器振动信号因非平稳特性而导致难以预测的问题,提出一种基于改进蜣螂优化算法的差分整合移动平均自回归预测模型。首先,利用ADF检验和KPSS检验对变压器原始振动信号进行平稳性检验,若不平稳则进行差分处理直至信号平稳。其次,通过在蜣螂优化算法中引入周期突变机制以提升算法的寻优能力,并利用改进后的蜣螂优化算法对差分整合移动平均自回归模型参数p和q进行定阶,实现对变压器振动信号的预测。最后,利用某个0.4-/0.4-k V,15-k VA三相双绕组干式变压器实际采集的振动数据,验证所提出模型的有效性。仿真结果表明,该模型的平均绝对百分比误差可达3.77%,而差分整合移动平均自回归模型、长短时记忆网络、循环神经网络和卷积神经网络的平均绝对百分比误差分别为5.34%、4.74%、5.03%、5.40%。因此,所提出的模型可以实现变压器振动信号的精准预测。

To solve the problem that power transformer vibration signals is difficult to predict because of the non-stationary characteristic,an autoregressive integrated moving average prediction model based on improved dung beetle optimizer algorithm is proposed.Firstly,ADF test and KPSS test are used to check the stationary of the transformer original vibration signal,and if it is not stationary,differential processing is performed until the signal is stationary.Secondly,the periodic mutation mechanism is introduced into dung beetle optimizer algorithm to improve the optimization ability of the algorithm,and the parameters p and q of autoregressive integrated moving average model are determined by improved dung beetle optimizer algorithm to realize the prediction of transformer vibration signal.Finally,the validity of the proposed model is verified by using the actual collected vibration data of a 0.4-/0.4-kV,15-kVA three-phase doublewinding dry-type transformer.The simulation result shows that the mean absolute percentage error of the model can reach 3.77%,while the mean absolute percentage error of the autoregressive integrated moving average model,long short-term memory network,recurrent neural network and convolutional neural network are 5.34%,4.74%,5.03% and 5.40%,respectively.Therefore,the proposed model can achieve accurate prediction of transformer vibration signal.