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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Wavelet-Like Transform to Optimize the Order of an Autoregressive Neural Network Model to Predict the Dissolved Gas Concentration in Power Transformer Oil from Sensor Data
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Wavelet-Like Transform to Optimize the Order of an Autoregressive Neural Network Model to Predict the Dissolved Gas Concentration in Power Transformer Oil from Sensor Data

机译：小波样变换可优化自回归神经网络模型的阶数从而根据传感器数据预测电力变压器油中的溶解气体浓度

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Dissolved gas analysis (DGA) is one of the most important methods to analyze fault in power transformers. In general, DGA is applied in monitoring systems based upon an autoregressive model; the current value of a time series is regressed on past values of the same series, as well as present and past values of some exogenous variables. The main difficulty is to decide the order of the autoregressive model; this means determining the number of past values to be used. This study proposes a wavelet-like transform to optimize the order of the variables in a nonlinear autoregressive neural network to predict the in oil dissolved gas concentration (DGC) from sensor data. Daubechies wavelets of different lengths are used to create representations with different time delays of ten DGC, which are then subjected to a procedure based on principal components analysis (PCA) and Pearson’s correlation to find out the order of an autoregressive model. The representations with optimal time delays for each DGC are applied as input in a multi-layer perceptron (MLP) network with backpropagation algorithm to predict the gas at the present and future times. This approach produces better results than choosing the same time delay for all inputs, as usual. The forecasts reached an average mean absolute percentage error (MAPE) of 5.763%, 1.525%, 1.831%, 2.869%, and 5.069% for C H , C H , C H , CH , and H , respectively.
机译：溶解气体分析（DGA）是分析电力变压器故障的最重要方法之一。通常，DGA被用于基于自回归模型的监视系统中。时间序列的当前值根据相同序列的过去值以及某些外生变量的当前值和过去值进行回归。主要困难在于确定自回归模型的顺序。这意味着确定要使用的过去值的数量。这项研究提出了一种类似小波的变换，以优化非线性自回归神经网络中变量的顺序，从而根据传感器数据预测油中溶解气体浓度（DGC）。使用不同长度的Daubechies小波来创建具有十个DGC的不同时延的表示，然后对它们进行基于主成分分析（PCA）和Pearson相关性的过程，以找出自回归模型的阶数。每个DGC的最佳时延表示形式都用作具有反向传播算法的多层感知器（MLP）网络中的输入，以预测当前和未来时间的气体。与通常为所有输入选择相同的时间延迟相比，此方法可产生更好的结果。预测的C H，C H，C H，CH和H的平均平均绝对百分比误差（MAPE）分别为5.763％，1.525％，1.831％，2.869％和5.069％。

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